Us, In the vast realm of technology, few concepts have captivated the human imagination as profoundly as Artificial Intelligence (AI). It is more than just a buzzword; it represents the pinnacle of human innovation. In this article, we delve into the intricate world of AI, understanding its core principles and exploring its myriad applications that are reshaping our world.
Understanding Artificial Intelligence
At its essence, Artificial Intelligence refers to the simulation of human intelligence in machines. It involves creating algorithms that enable computers to perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and language understanding. AI systems are designed to analyze patterns, process information, and make decisions, often outperforming human capabilities in specific domains.
Types of Artificial Intelligence
Narrow AI (Weak AI): This type of AI is designed to perform a specific task, such as language translation or playing chess. It operates within a limited context and excels in its predefined area.
General AI (Strong AI): General AI refers to a machine with the ability to understand, learn, and apply knowledge across diverse tasks, similar to human intelligence. Although it’s a topic of extensive research, true General AI is yet to be realized.
Artificial Superintelligence (ASI): ASI surpasses human intelligence and exhibits intellectual capabilities far beyond human comprehension. This level of AI remains theoretical and raises ethical and existential questions.
Applications of Artificial Intelligence
The applications of AI are vast and diverse, infiltrating various aspects of our lives and industries:
Healthcare: AI assists in diagnosing diseases, predicting outbreaks, and personalizing treatment plans. Machine learning algorithms analyze vast datasets to identify patterns, enabling early detection of illnesses and enhancing patient care.
Finance: AI algorithms predict market trends, optimize trading strategies, and detect fraudulent activities. Robo-advisors powered by AI offer personalized financial advice, making investment decisions more accessible to the general public.
Education: AI-driven platforms facilitate personalized learning experiences. Intelligent tutoring systems adapt to students’ needs, providing targeted assistance and feedback, thereby enhancing the learning process.
Autonomous Vehicles: AI technologies enable self-driving cars to perceive their surroundings, make real-time decisions, and navigate safely. These advancements hold the potential to revolutionize transportation, making it safer and more efficient.
Customer Service: AI-powered chatbots and virtual assistants handle customer queries, providing instant responses and enhancing user experience. Natural language processing allows these systems to understand and respond to human language effectively.
Manufacturing: AI-driven robotics streamline production processes, ensuring efficiency and precision. Predictive maintenance models anticipate equipment failures, reducing downtime and optimizing production schedules.
Entertainment: AI algorithms analyze user preferences to recommend movies, music, and content tailored to individual tastes. Additionally, AI is utilized in video game design, creating immersive and responsive gaming experiences.
Cybersecurity: AI detects and mitigates cybersecurity threats by analyzing network patterns and identifying anomalies. Machine learning algorithms enhance security measures, safeguarding sensitive data from potential breaches.
Conclusion
Artificial Intelligence stands at the forefront of technological evolution, shaping the future in ways previously deemed unimaginable. Its applications continue to expand, transforming industries and revolutionizing how we live, work, and interact. As we embrace the potential of AI, it is imperative to navigate the ethical implications, ensuring that this remarkable technology serves humanity’s best interests. In the coming years, the collaboration between human ingenuity and artificial intelligence promises to unlock unprecedented possibilities, ushering in an era of innovation and discovery.
If you ask somebody about AI these days, sooner rather than later, they will probably mention ChatGPT. Undoubtedly, ChatGPT is the most famous AI star on the red carpet these days. However, it’s important to know that there are other cool tools that are attending the ongoing AI revolution. In particular, one tool that can compete in popularity and capabilities with ChatGPT is Bard, the conversational large language model (LLM) developed by Google.
In this article, we will take you through the main features and differences between ChatGPT vs Google Bard. What is the underlying technology behind these powerful tools? Where are the pros and cons of ChatGPT vs Bard? In what areas do they perform better?
Keep reading to discover these answers and more!
Understanding Bard and ChatGPT
What is Bard?
Following the release of ChatGPT and its integration into Microsoft search engine Bing, it wouldn’t take much for Google to launch its own chatbot to compete with ChatGPT.
In February 2023, it announced the release of a new generative AI tool called Bard AI, powered by Google’s language model LaMDA.
However, as Google has reportedly admitted, Bard is still experimental, delivering only a fraction of its full potential. In the last months, Google has continuously upgraded Bard, powering it with the more powerful PaLM 2 model, offering improved performance in reasoning, coding, and multilingual capabilities. It’s well suited for creative tasks, provides cited sources, and it’s available for free.
In the coming versions, Bard will include multimodality, combining text and images in its answers. Equally, you will be able to include images in your prompts and integrate them with other Google tools, like YouTube, Maps, and Google Lens.
What is ChatGPT?
Developed in partnership with OpenAI and Microsoft, ChatGPT is an AI-powered chatbot designed to answer questions and perform tasks in a conversational way.
Launched in late 2022, ChatGPT took the world by storm with its unique and unprecedented ability to generate human-like text of all sorts, from code, document summaries, and lyrics to detailed essays, trip plans, and jokes. This is thanks to its underlying LLM, GPT3.5 in the free version of ChatGPT, and GPT4 in the pro, paid version.
The list of possibilities offered by ChatGPT is infinite, which is why, in just two months, it became the fastest-growing web application ever, reaching 100 million users. If you want to get your hands dirty with this wonderful AI tool, we highly recommend you check our Introduction to ChatGPT Course.
Key Similarities and Differences
Technically speaking, ChatGPT and Bard are very similar. They are both generative AI products with general-purpose capabilities that compete for the same market. They share the same underlying technology, the so-called LLMs.
OpenAI GPT-4 model powers the most advanced version of ChatGPT, while the Google counterpart is powered by the PaLM 2 model. To know more about LLMs, the foundational technology of the ongoing AI revolution, we highly recommend you to check out our Large Language Models (LLMs) Concepts Course.
However, when we compare Bard vs ChatGPT, we see many differences. We’ve summarised some of the most significant differences in the table below.
ChatGPT
Bard
Created by
OpenAI, Microsoft
Google
Underlying LLM
GPT-3.5 free versionGPT-4 Pro version
PaLM 2
Data sources
A wide collection of data sources from the Internet, including Wikipedia and scientific papers until 2021
Infiniset, a curated list of Internet content. Bard has a direct connection to up-to-date information through Google
Connected to the Internet
Only Pro version, through plugins.
Yes
Multimodality
Yes, image and voice inputs. Beta Image generation with new Dall-E integration
Yes, voice inputs and image outputs
Integration with other tools
Only Pro version, through plugins
Mainly Google tools, including Maps, Youtube, and Lens. More to come with brand-new plugins.
Price
FreePro: $20/month
Free
Diving Deeper into Bard
The mechanism behind Bard
Let’s get a bit more into the technicalities of Bard.
Initially, Bard was powered by the LaMDA family of language models, but it performed poorly compared to GPT-3.5, the LLM that powered the first ChatGPT. In order to compete with ChatGPT in the generative AI industry, Google has now switched to the more advanced PaLM 2 for all its AI products, including Bard.
The name “PaLM” refers to Pathways Language Model. PaLM leverages Google’s AI framework to teach machine learning models how to carry out various tasks. Unlike its predecessor, the PaLM model, PaLM 2 has been trained on a more diverse corpus of data, including an extensive number of human and programming languages, mathematical equations, scientific papers, and web pages. This allows PaLM 2 to deliver improved expertise in coding, enhanced logical reasoning, and mathematical abilities.
Another cool thing that distinguishes Bard from other chatbots is its internet access feature. Every time you prompt a question to Bard, it will browse Google to provide accurate and real-time responses. You can also use the “Google it” button to search for information on Google within Bard.
Bard can access up-to-date information through Google
Bard pros and cons
Since its official launch at the beginning of 2023, Bard has improved dramatically, and more is about to come as Google keeps integrating Bard within its powerful technology stack.
Among its current strengths, we can highlight:
Access to the internet.
Possibility to resume a previous chat.
Progressive integration with other Google tools, like YouTube, Maps, Lens, and Gmail.
Support over 40 human languages and more than 20 programming languages.
Voice inputs.
Text inputs enriched with images.
Multiple draft responses.
Free access.
However, as already mentioned, Google Bard is in its infancy, with weaknesses and limitations that require improvement in future versions, such as:
Inaccurate results, including hallucinations, especially with complex questions. To ensure higher accuracy, providing easily-interpretable prompts with sufficient context can make a big difference. Check out our article to discover the best practices in prompt engineering.
Lower performance in many tasks compared to other AI-powered chatbots.
Lack of sources for responses. However, it’s possible to use the “Google it” button to fact-check results.
Limited integrations with other tools. However, this will soon be possible following the recent announcement of Bard extensions.
Exploring ChatGPT
The technology powering ChatGPT
ChatGPT is a generative AI tool based on the GPT (Generative Pretrained Transformer) architecture. Developed by OpenAI, the GPT architecture extends to transformers with a focus on generating coherent and contextually accurate text. You can learn all the details about transformers in our Large Language Models Course.
The first version of ChatGPT, and still available as the free version, is based on the GPT-3.5 architecture. This is an upgraded version of the GPT-3 architecture model. The GPT-3 model is based on 175 billion parameters, providing unique capabilities in natural language processing and understanding tasks. By contrast, GPT-3.5 has fewer parameters to make it more efficient and is designed to work within policies based on human values.
The latest version of ChatGPT (GPT Plus) is based on GPT-4, a more advanced version of GPT-3. Although the specifics of GPT-4’s size and improvements are not publicly disclosed, it is expected to have even more parameters (some have estimated it has up to 1.76 trillion parameters), further fine-tuning its language generation abilities and performance across various tasks. Curious about the differences between GPT-3 and GPT-4? Check out our article, where we directly asked ChatGPT about the differences.
Since its first launch, OpenAI has been constantly upgrading ChatGPT, with promising innovations, such as the creation of the OpenAI GPT API for developers to build applications on top of OpenAI models, the possibility of third-party ChatGPT plugins, and the integration with other OpenAI LLMs. For example, OpenAI recently announced the integration of ChatGPT with Dall-E, the powerful image generation model.
ChatGPT Pros and cons
Using ChatGPT comes with many advantages, including:
Wide range of internal business use cases.
Possibility to resume a previous chat.
Ever-increasing catalog of plugins and integrations.
Support over 95 human languages and a wide variety of programming languages.
Voice inputs
Image generation
As for the cons of ChatGPT:
Only the ChatGPT Plus version has access to real-time information.
Sometimes inaccuracy or false claims, including LLM hallucinations.
Lack of sources for responses.
Privacy and copy-rights concerts regarding the training data.
Paid version to use ChatGPT at full capacity.
Bard vs. ChatGPT: A Comparative Analysis
Let’s analyze how Bard vs ChatGPT perform in different scenarios:
Data science and coding
Making data science accessible to everyone is at the core of DataCamp’s mission. When ChatGPT and Bard launched the tech space, we wondered whether these next-generation tools could help us achieve this mission.
Over recent months, we have tested Google Bard and ChatPT in a wide variety of data science and coding tasks. Overall, our analyses show that ChatGPT, especially its Pro version powered by GPT-4, is currently and overall a more suitable tool for data science and coding tasks than Google Bard. This applies to a wide range of areas, including programming workflows, data analysis, data visualization, machine learning, and natural language processing tasks. This is largely due to the ChatGPT Code Interpreter feature.
Both ChatGPT vs Google Bard stand out as great research assistants. They can help you in many aspects, including suggesting and framing research questions, performing preliminary literature reviews, or rewriting texts to improve clarity and accessibility.
However, they are still very far from replacing the work of researchers. Despite their impressive capabilities, neither ChatGPT nor Bard would meet the quality standards for academic publishing.
First, there’s the question of reliability. These tools often provide misleading, inaccurate information, and can fall into LLM hallucination out of the blue (generating information that is incorrect, misleading, or not based on real-world facts). This kind of behavior is unacceptable in a task like academic research, where everything is about precision and integrity.
This links to the second problem of these tools: authenticity and authorship. Scientific research is an evolving process where every new idea is grounded on previous, accumulated research. To support their ideas, researchers always have to provide references to previous research on the topic.
A common problem of Bard and ChatGPT is the absence of citations or references of the source. While Bard provides citations when it directly quotes at length from a webpage, more is needed in scientific research, where every statement based on previous knowledge needs to be referenced.
Finally, to provide relevant scientific publications, researchers have to be aware of the latest publications. However, this is not possible with ChatGPT, at least in its free version, as it is not connected to the internet, and its knowledge doesn’t go beyond September 2021, as shown in the image below.
In this regard, the possibility of Google Bard providing up-to-date information makes it a better research assistant than ChatGPT.
Writing assistance
The underlying technology of ChatGPT and Google Bard is specifically designed to excel in writing tasks. Every writing task you can think of can be performed by these tools, from writing essays, cooking recipes, and newsletters to poems, summaries, and even jokes.
It’s difficult to measure what tool provides the best replies, for both ChatGPT and Bard have great quality standards.
However, some differences are worth mentioning. First, the word limit. Currently, the free version of ChatGPT has a word limit of around 500 words or 4000 characters. The pro version comes with more tokens. By contrast, Google Bard claims not to have a word limit for answers. This can make a great difference, especially if you expect these tools to provide lengthy answers.
Another interesting feature that makes Google Bard different from ChatGPT is its ability to provide multiple draft answers, so you can choose the one you like the most.
User experience
Overall, both ChatGPT and Bard are simple, easy-to-use, and intuitive tools. They come with a user-friendly, chat-based interface where you just have to write your prompt as if you were speaking to a friend on WhatsApp. Both also include a sidebar where you can resume previous chats.
Hence, we cannot say one is better than the other. In my case, I like the Google Bard interface better, but it’s just a matter of personal taste.
Integration, plugins, and collaboration
To become the leader in the new LLM-powered chatbot market, you need a huge pool of users. Not only direct users who use the tool to prompt questions but also developers who can plug their applications on top of the chatbot.
This is something that OpenAI understood from the very beginning, and that’s why the company rapidly launched OpenAI GPT API, a tool that enables developers to integrate the power of ChatGPT into their applications, products, or services.
The same idea underpinned the announcement of ChatGPT plugins. Aimed at unlocking the vast range of possible use cases of ChatGPT, plugins are designed to add functionality and features, such as access to up-to-date information, run computations, or use third-party services. So far, ChatGPT is testing plugins with a limited number of developers and is only available for Pro versions.
Google Bard is taking a different strategy to achieve the same goal. The core advantage of Google is its widely-used technology stack. To increase the pool of users of Bard, Google is steadily integrating the tool into other Google applications, including Drive, Gmail, Maps, and Google Lens.
Also, Google recently announced the launch of Bard third-party plugins, also called Bard extensions, which will allow new integrations with third-party services and applications.
Images
An important difference between Bard vs ChatGPT is the ability of Bard to provide answers with images. This is a cool feature to enrich the generated text.
By contrast, ChatGPT has recently increased its image generation capabilities by integrating Dall-E. This will allow ChatGPT Plus users to describe an image and ChatGPT will create it, as shown below:
ChatGPT vs Bard: Practical Applications and Use-Cases
Bard in action
Although Bard is still in an experimental phase, it is already one of the best chatbots in the market. You can use it in a wide range of scenarios, from customer support and medical assistance to e-learning and content creation.
However, the full potential of Bard will be unblocked as it keeps its integration process with other Google tools. This will make Bard a unique chatbot. Thanks to these integrations with popular and widely-used Google services, Bard will be able to find, combine, and show relevant information from different sources, including Gmail, Docs, Drive, Google Maps, and YouTube.
For example, if you’re planning a business trip to the Greek Islands (a project that takes up many tabs), you can now ask Bard to recommend the dates that work for everyone from Gmail, look up real-time flight and hotel information, see Google Maps directions to the airport, and even watch YouTube videos of things to do there.
OpenAI has managed to keep its momentum after the initial launch of ChatGPT. Today, ChatGPT is no doubt the most popular and widely used LLM-powered chatbot. The use cases are endless and will be more as developers create new plugins and build their applications on top of the OpenAI GPT API.
For example, at DataCamp we are already leveraging ChatGPT in our cloud-based IDE, DataCamp Workspace. With the new ChatGPT-based AI Assistant, Workspace aims to make data science even more accessible and productive for its users. Key features of the AI Assistant include the “Fix Error” button, which not only corrects code errors but also explains them, allowing users to learn and avoid repeating mistakes.
Either as a standalone product or as the foundation model of other applications, ChatGPT is leading the generative AI revolution, and more is to come as the product keeps evolving with more powerful technology. For example, there are already conversations on the next-generation GPT-5 that may reach the market in 2024.
Conclusion: ChatGPT vs Google Bard, Which is Better?
With the launch of ChatGPT in late 2022, OpenAI took everyone by storm. One year later, OpenAI has managed to keep its momentum. ChatGPT is still by far the most popular and widely-used LLM-powered chatbot, and this is unlikely to change in the near future, especially if OpenAI keeps improving the product with new features. This is the case with the recently announced integration with Dall-E, which will help ChatGPT to compete with other image generation tools, like Midjourney.
Yet while Bard’s PaLM 2 model doesn’t seem to rival the GPT-4 model that powers the most advanced version of ChatGPT, Bard is much more than a standalone chatbot. With its vast pool of users, Bard already has the most important ingredient for success. As the tool keeps integrating with other Google products, Bard can easily become ubiquitous, and once users get used to it, sooner or later, they will start using it. Hence, in the long run, Bard may be able to overcome ChatGPT.
In the meantime, if you want to keep posted on the latest developments in generative AI, at DataCamp, we have you covered. Check out our dedicated materials and start your generative AI journey today:
As one of the most popular programming languages out there, many people want to learn Python. But how do you go about getting started? In this guide, we explore everything you need to know to begin your learning journey, including a step-by-step guide and learning plan and some of the most useful resources to help you succeed.
Python is a high-level, interpreted programming language created by Guido van Rossum and first released in 1991. It is designed with an emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C++ or Java.
Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. In simpler terms, this means it’s flexible and allows you to write code in different ways, whether that’s like giving the computer a to-do list (procedural), creating digital models of things or concepts (object-oriented), or treating your code like a math problem (functional).
What makes Python so popular?
As of July 2023, Python remains the most popular programing language according to the TIOBE index. Over the years, Python has become one of the most popular programming languages due to its simplicity, versatility, and wide range of applications.
The popularity of Python
These reasons also mean it is a highly favored language for data science as it allows data scientists to focus more on data interpretation rather than language complexities.
Let’s explore these factors in more detail.
The main features of Python
Let’s have a close look at some of the Python features that make it such a versatile and widely-used programming language:
Readability. Python is known for its clear and readable syntax, which resembles English to a certain extent.
Easy to learn. Python’s readability makes it relatively easy for beginners to pick up the language and understand what the code is doing.
Versatility. Python is not limited to one type of task; you can use it in many fields. Whether you’re interested in web development, automating tasks, or diving into data science, Python has the tools to help you get there.
Rich library support. It comes with a large standard library that includes pre-written code for various tasks, saving you time and effort. Additionally, Python’s vibrant community has developed thousands of third-party packages, which extend Python’s functionality even further.
Platform independence. One of the great things about the language is that you can write your code once and run it on any operating system. This feature makes Python a great choice if you’re working on a team with different operating systems.
Interpreted language. Python is an interpreted language, which means the code is executed line by line. This can make debugging easier because you can test small pieces of code without having to compile the whole program.
Open source and free. It’s also an open-source language, which means its source code is freely available and can be distributed and modified. This has led to a large community of developers contributing to its development and creating a vast ecosystem of Python libraries.
Dynamically typed. Python is dynamically typed, meaning you don’t have to declare the data type of a variable when you create it. The Python interpreter infers the type, which makes the code more flexible and easy to work with.
Why is learning Python so beneficial?
Learning Python is beneficial for a variety of reasons. Besides its wide popularity, Python has applications in numerous industries, from tech to finance, healthcare, and beyond. Learning Python opens up many career opportunities and guarantees improved career outcomes. Here’s how:
Python has a variety of applications
We’ve already mentioned the versatility of Python, but let’s look at a few specific examples of where you can use it:
Data science. Python is widely used in data analysis and visualization, with libraries like Pandas, NumPy, and Matplotlib being particularly useful.
Web development. Frameworks such as Django and Flask are used for backend web development.
Software development. You can use Python in software development for scripting, automation, and testing.
Game development. You can even use it for game development using libraries like PyGame and tkinter.
Machine learning & AI. Libraries like TensorFlow, PyTorch, and Scikit-learn make Python a popular choice in this field. Find out how to learn AI in a separate guide.
There is a demand for Python skills
With the rise of data science, machine learning, and artificial intelligence, there is a high demand for Python skills. According to a 2022 report from GitHub, Python usage increased 22.5% year on year, making it the third-most used language on the platform.
Companies across many industries are looking for professionals who can use Python to extract insights from data, build machine learning models, and automate tasks.
Learning Python can significantly enhance your employability and open up a wide range of career opportunities. A quick search on the recruitment website Indeed for ‘Python’ finds nearly 10,000 jobs in the US requiring the skill.
How Long Does it Take to Learn Python?
While Python is one of the easier programming languages to learn, it still requires dedication and practice. The time it takes to learn Python can vary greatly depending on your prior experience with programming, the complexity of the concepts you’re trying to grasp, and the amount of time you can dedicate to learning.
However, with a structured learning plan and consistent effort, you can often grasp the basics in a few weeks and become somewhat proficient in a few months.
Online resources can give you a firm basis for your skills and can range in length. As an example, our Python Programming skill track, covering the skills needed to code proficiently, takes around 24 study hours to complete, while our Data Analyst with Python career track takes around 36 study hours. Of course, the journey to becoming a true Pythonista is a long-term process, and much of your efforts will need to be self-study alongside more structured methods.
As a comparison of how long it takes to learn Python vs other languages:
Language
Time to Learn
Python
1-3 months for basics, 4-12 months for advanced topics
SQL
1 to 2 months for basics, 1-3 months for advanced topics
R
1-3 months for basics, 4-12 months for advanced topics
Julia
1-3 months for basics, 4-12 months for advanced topics
* The above comparisons are purely based on timelines needed to learn to become proficient with a programming language, not timelines needed to break into a career. Moreover, each person learns differently and goes at their own pace, we only aim to provide a framework with these timelines.
A comparison table of how long it would take to learn different programming languages
How to Learn Python: 6 Steps for Success
Let’s take a look at how you can go about learning Python. This step-by-step guide assumes you’re at learning Python from scratch, meaning you’ll have to start with the very basics and work your way up.
1. Understand why you’re learning Python
Firstly, it’s important to figure out your motivations for wanting to learn Python. It’s a versatile language with all kinds of applications. So, understanding why you want to learn Python will help you develop a tailored learning plan.
Whether you’re interested in automating tasks, analyzing data, or developing software, having a clear goal in mind will keep you motivated and focused on your learning journey. Some questions to ask yourself might include:
What are my career goals? Are you aiming for a career in data science, web development, software engineering, or another field where Python is commonly used?
What problems am I trying to solve? Are you looking to automate tasks, analyze data, build a website, or create a machine learning model? Python can be used for all these tasks and more.
What interests me? Are you interested in working with data or building applications? Or perhaps you’re intrigued by artificial intelligence? Your interests can guide your learning journey.
What is my current skill level? If you’re a beginner, Python’s simplicity and readability make it a great first language. If you’re an experienced programmer, you might be interested in Python because of its powerful libraries and frameworks.
The answers to these questions will determine how to structure your learning path, which is especially important for the following steps.
Python is one of the easiest programming languages to pick up. What’s really nice is that learning Python doesn’t pigeonhole you into one domain; Python is so versatile it has applications in software development, data science, artificial intelligence, and almost any role that has programming involved with it!
Richie Cotton, Data Evangelist at DataCamp
2. Get started with the Python basics
Understanding Python Basics
Python emphasizes code readability and allows you to express concepts in fewer lines of code. You’ll want to start by understanding basic concepts such as variables, data types, and operators.
Our Introduction to Python course covers the basics of Python for data analysis, helping you get familiar with these concepts.
Installing Python and setting up your environment
To start coding in Python, you need to install Python and set up your development environment. You can download Python from the official website, use Anaconda Python, or start with DataCamp Workspace to get started with Python in your browser.
DataCamp Workspace
Skip the installation process, and get started with Python on your browser using DataCamp Workspace
Start by writing a simple Python program, such as a classic “Hello, World!” script. This process will help you understand the syntax and structure of Python code. Our Python tutorial for beginners will take you through some of these basics.
Python data structures
Python offers several built-in data structures like lists, tuples, sets, and dictionaries. These data structures are used to store and manipulate data in your programs. We have a course dedicated to data structures and algorithms in Python, which covers a wide range of these aspects.
Control flow in Python
Control flow statements, like if-statements, for-loops, and while-loops, allow your program to make decisions and repeat actions. We have a tutorial on if statements, as well as ones on while-loops and for-loops.
Functions in Python
Functions in Python are blocks of reusable code that perform a specific task. You can define your own functions and use built-in Python functions. We have a course on writing functions in Python which covers the best practices for writing maintainable, reusable, complex functions.
3. Master intermediate Python concepts
Once you’re familiar with the basics, you can start moving on to some more advanced topics. Again, these are essential for building your understanding of Python and will help you tackle an array of problems and situations you may encounter when using the programming language.
Error handling and exceptions
Python provides tools for handling errors and exceptions in your code. Understanding how to use try/except blocks and raise exceptions is crucial for writing robust Python programs. We’ve got a dedicated guide on exception and error handling in Python which can help you troubleshoot your code.
Working with libraries in Python
Python’s power comes from its vast ecosystem of libraries. Learn how to import and use common libraries like NumPy for numerical computing, pandas for data manipulation, and matplotlib for data visualization. In a separate article, we cover the top Python libraries for data science, which can provide more context for these tools.
Object-oriented programming in Python
Python supports object-oriented programming (OOP), a paradigm that allows you to structure your code around objects and classes. Understanding OOP concepts like classes, objects, inheritance, and polymorphism can help you write more organized and efficient code.
To learn more about object-oriented programming in Python, check out our online course, which covers how to create classes and leverage techniques such as inheritance and polymorphism to reuse and optimize your code.
4. Learn by doing
One of the most effective ways to learn Python is by actively using it. You want to minimize the amount of time you spend on learning syntax and work on projects as soon as possible. This learn-by-doing approach involves applying the concepts you’ve learned through your studies to real-world projects and exercises.
Thankfully, many DataCamp resources use this learn-by-doing method, but here are some other ways to practice your skills:
Take on projects that challenge you. Work on projects that interest you. This could be anything from a simple script to automate a task, a data analysis project, or even a web application.
Attend webinars and code-alongs. You’ll find plenty of DataCamp webinars and online events where you can code along with the instructor. This method can be a great way to learn new concepts and see how they’re applied in real-time.
Apply what you’ve learned to your own ideas and projects. Try to recreate existing projects or tools that you find useful. This can be a great learning experience as it forces you to figure out how something works and how you can implement it yourself.
A range of Python projects on DataCamp Projects
5. Build a portfolio of projects
As you complete projects, compile them into a portfolio. This portfolio should reflect your skills and interests and be tailored to the career or industry you’re interested in. Try to make your projects original and showcase your problem-solving skills.
We’ve got a list of 60+ Python projects for all levels in a separate article, but here are a few suggested project ideas for different levels:
Beginners. Simple projects like a number guessing game, a to-do list application, or a basic data analysis using a dataset of your interest.
Intermediate. More complex projects like a web scraper, a blog website using Django, or a machine learning model using Scikit-learn.
Advanced. Large-scale projects like a full-stack web application, a complex data analysis project, or a deep learning model using TensorFlow or PyTorch.
Never stop learning. Once you’ve mastered the basics, look for more challenging tasks and projects. Specialize in areas that are relevant to your career goals or personal interests. Whether it’s data science, web development, or machine learning, there’s always more to learn in the world of Python. Remember, the journey of learning Python is a marathon, not a sprint. Keep practicing, stay curious, and don’t be afraid to make mistakes.
An Example Python Learning Plan
Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’re just starting out with Python. Remember, the timescales, subject areas, and progress all depend on a wide range of variables. We want to make this plan as hands-on and practical as possible, which is why we’ve recommended projects you can work on as you progress.
Month 1-3: Basics of Python and data manipulation
Master basic and intermediate programming concepts. Start doing basic projects in your specialized field. For example, if you’re interested in data science, you might start by analyzing a dataset using pandas and visualizing the data with matplotlib.
Python basics. Start with the fundamentals of Python. This includes understanding the syntax, data types, control structures, functions, and more.
Data manipulation. Learn how to handle and manipulate data using Python libraries like pandas and NumPy. This is a crucial skill for any Python-related job, especially in data science and machine learning.
Now that you have a solid foundation, you can start learning more advanced topics.
Intermediate Python. Once you’re comfortable with the basics, move on to more advanced Python topics. This includes understanding object-oriented programming, error handling, and more complex data structures. Explore more advanced topics like decorators, context managers, metaclasses, and more.
More specific topics. If you’re interested in machine learning, for example, you might start the Machine Learning Fundamentals with Python Track. Continue to work on projects, but make them more complex. For example, you might build a machine learning model to predict house prices or classify images.
Month 7 onwards: Advanced Python and specialization
At this point, you should have a good understanding of Python and its applications in your field of interest. Now is the time to specialize.
Specialization. Based on your interests and career aspirations, specialize in one area. This could be data science, machine learning, web development, automation, or any other field. For instance, If you’re interested in natural language processing, you might start learning about libraries like NLTK and SpaCy. Keep working on projects and reading about new developments in your field.
If you’re eager to start your Python learning journey, it’s worth bearing these tips in mind; they’ll help you maximize your progress and keep focused.
1. Choose Your Focus
Python is a versatile language with a wide range of applications, from web development and data analysis to machine learning and artificial intelligence. As you start your Python journey, it can be beneficial to choose a specific area to focus on. This could be based on your career goals, personal interests, or simply the area you find most exciting.
Choosing a focus can help guide your learning and make it more manageable. For example, if you’re interested in data science, you might prioritize learning libraries like pandas and NumPy. If web development is your goal, you might focus on frameworks like Django or Flask.
Remember, choosing a focus doesn’t mean you’re limited to that area. Python’s versatility means that skills you learn in one area can often be applied in others. As you grow more comfortable with Python, you can start exploring other areas and expanding your skill set.
2. Practice regularly
Consistency is a key factor in successfully learning a new language, and Python is no exception. Aim to code every day, even if it’s just for a few minutes. This regular practice will help reinforce what you’ve learned, making it easier to recall and apply.
Daily practice doesn’t necessarily mean working on complex projects or learning new concepts each day. It could be as simple as reviewing what you’ve learned, refactoring some of your previous code, or solving coding challenges.
3. Work on real projects
The best way to learn Python is by using it. Working on real projects gives you the opportunity to apply the concepts you’ve learned and gain hands-on experience. Start with simple projects that reinforce the basics, and gradually take on more complex ones as your skills improve. This could be anything from automating a simple task, building a small game, or even creating a data analysis project.
4. Join a community
Learning Python, like any new skill, doesn’t have to be a solitary journey. In fact, joining a community of learners can provide a wealth of benefits. It can offer support when you’re facing challenges, provide motivation to keep going, and present opportunities to learn from others.
There are many Python communities you can join. These include local Python meetups, where you can connect with other Python enthusiasts in person and online forums where you can ask questions, share your knowledge, and learn from others’ experiences.
5. Don’t rush
Learning to code takes time, and Python is no exception. Don’t rush through the material in an attempt to learn everything quickly. Take the time to understand each concept before moving on to the next. Remember, it’s more important to fully understand a concept than to move through the material quickly.
6. Keep iterating
Learning Python is an iterative process. As you gain more experience, revisit old projects or exercises and try to improve them or do them in a different way. This could mean optimizing your code, implementing a new feature, or even just making your code more readable. This process of iteration will help reinforce what you’ve learned and show you how much you’ve improved over time.
The Best Ways to Learn Python in 2023
There are many ways that you can learn Python, and the best way for you will depend on how you like to learn and how flexible your learning schedule is. Here are some of the best ways you can start learning Python from scratch today:
Online courses
Online courses are a great way to learn Python at your own pace. We offer over 150 Python courses for all levels, from beginners to advanced learners. These courses often include video lectures, quizzes, and hands-on projects, providing a well-rounded learning experience.
If you’re totally new to Python, you might want to start with our Introduction to Python course. For those looking to grasp all the essentials, our Python Fundamentals skill track covers everything you need to start programming.
Tutorials are a great way to learn Python, especially for beginners. They provide step-by-step instructions on how to perform specific tasks or understand certain concepts in Python.
We have a wide range of tutorials available related to Python and associated libraries. So whether you’re just getting started or hoping to improve your existing knowledge, you’re sure to find topics of interest.
If you’re looking for a fast way to brush up on specific Python principles, cheat sheets are a handy way to have a lot of knowledge in one resource. For example, our Python Cheat Sheet for Beginners covers many of the core concepts you’ll need to get started.
We also have cheat sheets for specific Python libraries, such as Seaborn and SciPy, which include example code snippets and tips to get the most out of the tools.
Working on projects helps you utilize the skills you’ve learned already to tackle new challenges. As you work your way through, you’ll need to adapt your approach and research new ways of getting results, helping you to master new Python techniques.
You can find a whole range of data science projects to work on at DataCamp. These allow you to apply your coding skills to a wide range of datasets to solve real-world problems in your browser, and you can filter specifically by those that require Python.
Books are an excellent resource for learning Python, especially for those who prefer self-paced learning. Learn Python the Hard Way by Zed Shaw and Python Crash Course by Eric Matthes are two highly recommended books for beginners. These books provide in-depth explanations of Python concepts along with numerous exercises and projects to reinforce your learning.
As we’ve already seen, demand for professionals with Python skills is increasing, and there are many roles out there that require knowledge of the programming language. Here are some of the top careers that use Python you can choose from:
Data scientist
Data scientists are the detectives of the data world, responsible for unearthing and interpreting rich data sources, managing large amounts of data, and merging data points to identify trends.
They utilize their analytical, statistical, and programming skills to collect, analyze, and interpret large datasets. They then use this information to develop data-driven solutions to challenging business problems.
Part of these solutions is developing machine learning algorithms that generate new insights (e.g., identifying customer segments), automate business processes (e.g., credit score prediction), or provide customers with newfound value (e.g., recommender systems).
Key skills:
Strong knowledge of Python, R, and SQL
Understanding of machine learning and AI concepts
Proficiency in statistical analysis, quantitative analytics, and predictive modeling
Data visualization and reporting techniques
Effective communication and presentation skills
Essential tools:
Data analysis tools (e.g., pandas, NumPy)
Machine learning libraries (e.g., Scikit-learn)
Data visualization tools (e.g., Matplotlib, Tableau)
Big data frameworks (e.g., Airflow, Spark)
Command line tools (e.g., Git, Bash)
Python developer
Python developers are responsible for writing server-side web application logic. They develop back-end components, connect the application with the other web services, and support the front-end developers by integrating their work with the Python application. Python developers are also often involved in data analysis and machine learning, leveraging the rich ecosystem of Python libraries.
Key skills:
Proficiency in Python programming
Understanding of front-end technologies (HTML, CSS, JavaScript)
Knowledge of Python web frameworks (e.g., Django, Flask)
Familiarity with ORM libraries
Basic understanding of database technologies (e.g., MySQL, PostgreSQL)
Essential tools:
Python IDEs (e.g., PyCharm)
Version control systems (e.g., Git)
Python libraries for web development (e.g., Django, Flask)
Data analyst
Data analysts are responsible for interpreting data and turning it into information that can offer ways to improve a business. They gather information from various sources and interpret patterns and trends. Once data has been gathered and interpreted, Data analysts can then report back what they’ve found to the wider business to influence strategic decisions.
Key skills:
Proficiency in Python, R, and SQL
Strong knowledge of statistical analysis
Experience with business intelligence tools (e.g., Tableau, Power BI)
Understanding of data collection and data cleaning techniques
Effective communication and presentation skills
Essential tools:
Data analysis tools (e.g., pandas, NumPy)
Business intelligence data tools (e.g., Tableau, Power BI)
SQL databases (e.g., MySQL, PostgreSQL)
Spreadsheet software (e.g., MS Excel)
Machine learning engineer
Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge. These professionals are responsible for creating programs and algorithms that enable machines to take action without being specifically directed to perform those tasks.
Key skills:
Proficiency in Python, R, and SQL
Deep understanding of machine learning algorithms
Knowledge of deep learning frameworks (e.g., TensorFlow,)
A degree can be a great asset when starting a career that uses Python, but it’s not the only pathway. While a formal education in computer science or a related field can be beneficial, more and more professionals are entering the field through non-traditional routes. With dedication, consistent learning, and a proactive approach, you can land your dream job that uses Python.
Here’s how to find a job that uses Python without a degree:
Keep learning about the field
Stay updated with the latest developments in Python. Follow influential Python professionals on Twitter, read Python-related blogs, and listen to Python-related podcasts. Some of the Python thought leaders to follow include Guido van Rossum (the creator of Python), Raymond Hettinger, and others. You’ll gain insights into trending topics, emerging technologies, and the future direction of Python.
You should also check out industry events, whether it’s webinars at DataCamp, Python conferences, or networking events.
Develop a portfolio
Building a strong portfolio that demonstrates your skills and completed projects is one way to differentiate yourself from other candidates. Importantly, showcasing projects where you’ve applied Python to address real-world challenges can leave a lasting impression on hiring managers.
The key to standing out is to show your project made an impact and show that other people cared. Why are we in data? We’re trying to find insights that actually impact a business, or we’re trying to find insights that will actually shape society or create something novel. We’re trying to improve profitability or improve people’s lives using and analyzing data, so if you don’t somehow quantify the impact, then you are lacking impact.
Nick Singh, Author of Ace the Data Science Interview
Your portfolio should be a diverse showcase of projects that reflect your Python expertise and its various applications. For further guidance on crafting an impressive data science portfolio, refer to our dedicated article on the topic.
Develop an effective resume
In the modern job market, your resume needs to impress not just human recruiters but also Applicant Tracking Systems (ATS). These automated software systems are used by many companies to sift through resumes and eliminate those that don’t meet specific criteria. As a result, it’s essential to optimize your resume to be both ATS-friendly and compelling to hiring managers.
According to Jen Bricker, former Head of Career Services at DataCamp:
60% to 70% of applications get shifted out of consideration before humans actually look at the application.
Jen Bricker, Former Head of Career Services at DataCamp
Therefore, it’s crucial to structure your resume as effectively as possible. For more insights on creating a standout data scientist resume, check out our separate article on the subject.
Get noticed by hiring managers
Proactive engagement on social platforms can help you catch the attention of hiring managers. Share your projects and thoughts on platforms like LinkedIn or Twitter, participate in Python communities, and contribute to open-source projects. These activities not only increase your visibility but also demonstrate your enthusiasm for Python.
Remember, forging a career in a field that utilizes Python requires persistence, ongoing learning, and patience. But by following these steps, you’re well on your way to success.
Final Thoughts
Learning Python is a rewarding journey that can open up a multitude of career opportunities. This guide has provided you with a roadmap to start your Python learning journey, from understanding the basics to mastering advanced concepts and working on real-world projects.
Remember, the key to learning Python (or any programming language) is consistency and practice. Don’t rush through the concepts. Take your time to understand each one and apply it in practical projects. Join Python communities, participate in coding challenges, and never stop learning.
We’re living through what is quite possibly a pivotal point in human history, where the importance of Artificial Intelligence (AI) is becoming increasingly undeniable. Just consider this statistic: 97% of business owners expect that ChatGPT will bring about positive changes in at least one area of their business, according to a survey by Forbes Advisor. Moreover, tools like ChatGPT, Midjourney, and Bard are ushering AI into the mainstream. This makes the art and science of AI more relevant than ever before.
If you’re an aspiring data scientist, machine learning engineer, AI researcher, or simply an AI enthusiast, this guide is for you. Throughout this article, we’ll detail how to learn AI from scratch and offer insights from industry experts to help steer your journey. As well as covering the skills and tools you’ll need to master, we also explore how businesses can leverage AI in today’s landscape.
What is Artificial Intelligence (AI)?
AI, or Artificial Intelligence, is a branch of computer science focused on creating systems that can perform tasks that would normally require human intelligence. These tasks range from understanding natural language, recognizing patterns, making decisions, and learning from experience. AI is a broad field with numerous subfields, each with its unique objectives and specializations. Check out our full guide, What is AI? to find out more. You can also explore how AI is different from machine learning in a separate article.
What are the different types of artificial intelligence?
As AI grows in popularity, the technology is discussed in various ways. To simplify the remainder of the article, it’s important to look at the different types of AI. AI can be categorized into three levels based on its capabilities:
Artificial Narrow Intelligence (ANI): This is the most common form of AI we interact with today. ANI is designed to perform a single task, like voice recognition or recommendations on streaming services.
Artificial General Intelligence (AGI): An AI with AGI possesses the ability to understand, learn, adapt, and implement knowledge across a wide range of tasks at a human level. While large language models and tools such as ChatGPT have shown the ability to generalize across many tasks—as of 2023, this is still a theoretical concept.
Artificial Super Intelligence (ASI): The final level of AI, ASI, refers to a future scenario where AI surpasses human intelligence in nearly all economically valuable work. This concept, while intriguing, remains largely speculative.
The difference between data science, artificial intelligence, machine learning & deep learning
If you are new to this topic, you may also see the terms “machine learning,” “deep learning,” “data science,” and others creep into AI discourse. AI is a broad field with several subsets, including Machine Learning (ML) and Deep Learning (DL).
While there isn’t an official definition for any of these terms, and while experts argue over the exact boundaries, there is a growing consensus on the broad scope of each term. Here’s a breakdown of how these terms can be defined:
Artificial intelligence refers to computer systems that can behave intelligently, reason, and learn like humans.
Machine learning is a subset of artificial intelligence focused on developing algorithms with the ability to learn without explicitly being programmed.
Deep learning is a subset of machine learning. It is responsible for many of the awe-inspiring news stories about AI in the news (e.g., self-driving cars, ChatGPT). Deep learning algorithms are inspired by the brain’s structure and work exceptionally well with unstructured data such as images, videos, or text.
Data science is a cross-disciplinary field that uses all of the above, amongst other skills like data analysis, statistics, data visualization, and more, to get insight from data.
Why Learn Artificial Intelligence Right Now?
Artificial Intelligence is more than just a buzzword; it’s a revolutionary technology transforming how we work, live, and interact. With the explosion of data and the need to make sense of it, the demand for AI skills is skyrocketing. There’s no better time than now to start learning AI. Here’s why:
AI is a fast-growing field
Artificial Intelligence isn’t the future; it’s the present. The number of AI jobs has seen significant growth over recent years. According to the World Economic Forum’s Future of Jobs report, AI and machine learning specialists top the list of fast-growing jobs over the next five years. As industries continue to adopt AI technologies to streamline their operations and make better decisions, the demand for AI specialists will likely only increase.
This prediction is further emphasized in the projected AI market size growth between 2021 and 2030, as seen in the graph below.
Naturally, the surge in demand for AI skills comes with attractive compensation. According to data from Glassdoor, as of 2023, the average salary of an AI engineer in the United States is $128,479 per annum, with the potential for bonuses and profit sharing. Machine learning engineers and data scientists are similarly well-paid, with average salaries of $133,111 and $126,888 per annum, respectively. This financial compensation reflects the value and impact of AI skills in the marketplace.
Artificial Intelligence is not just about high-paying jobs and significant market demand. It’s also an intellectually stimulating field that promises to challenge you in exciting ways. It involves building algorithms to solve complex problems, designing models that simulate human intelligence, and creatively applying these technologies to various real-world scenarios.
AI professionals continuously learn, adapt, and innovate. The field is constantly evolving, meaning there’s always something new to learn, a problem to solve, or a system to improve. This dynamic nature makes AI an exciting field for those who thrive on challenges and continuous learning.
How Long Does it Take to Learn AI?
The time it takes to learn AI will often depend on the route you take; whether it’s self-taught or through formal education such as a university program.
In a self-taught route, the duration can vary significantly as it largely depends on your prior knowledge, dedication, and available learning resources. It can take several months to a year or more to gain a solid understanding of AI concepts, programming languages such as Python, mathematics, and various machine learning algorithms through self-study. Self-paced online courses, tutorials, and practical projects can accelerate the learning process.
On the other hand, a university route typically involves pursuing a formal education in computer science, data science, or related fields. A bachelor’s degree in these disciplines typically takes around three to four years to complete, during which students receive comprehensive training in AI and related subjects.
Whichever route you choose, continuous learning, practical application, and staying up-to-date with advancements are essential for pursuing a career in AI.
How to Learn AI From Scratch in 2023
Learning AI can be an exciting endeavor, but it’s not without its challenges. It’s a broad field with many subtopics. However, with a clear roadmap, the right resources, and a strategic approach, you can navigate this landscape effectively. Here’s how to learn AI in 2023:
1. Master the prerequisite skills
Before diving into AI, you’ll want to have a grounding in certain areas. These foundational skills will form the backbone of your AI understanding and capabilities:
Basic mathematics. AI relies heavily on mathematical concepts, particularly its subfields like machine learning and deep learning. Of course, you don’t have to be a mathematician to succeed in AI, but a basic understanding of linear algebra, calculus, and probability is essential. For instance, concepts such as matrices and linear transformations from linear algebra are frequently used in AI algorithms.
Basic statistics. AI makes a lot of sense when you understand statistics. Knowing how to interpret data and derive insights is crucial in this field. Concepts like statistical significance, distribution, regression, and likelihood play a significant role in different AI applications.
A willingness to learn. AI is a rapidly evolving field with new advancements, techniques, and tools constantly emerging. Therefore, a proactive mindset and an enthusiasm for learning and adapting to new knowledge and technologies are pivotal for anyone looking to break into and advance in AI.
It’s important to note that the depth of understanding and mastery required in these prerequisite areas can vary depending on the AI role you aim to pursue. For instance, a data scientist might not need an in-depth understanding of every mathematical concept used in AI, but a research scientist aiming to create new AI algorithms might need a more profound grasp of mathematics.
The key is to align your learning path with your career goals and adjust the depth of your learning in different areas accordingly.
2. Develop specialist AI skills
Now that we’ve covered the prerequisites, let’s delve into the essential skills you need to develop to master AI. Similar to the prerequisite section above—the level of mastery you’ll need for these skills largely depends on what type of role you wish to pursue.
Statistics
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. It provides the backbone for understanding and working with data in AI.
Learn more about statistics in the resources below:
Statistics Fundamentals with Python Skill Track, where you’ll learn the four fundamentals of statistics using Python, including summary statistics and probability, statistical models such as linear and logistic regression, techniques for sampling, how to perform hypothesis tests, and draw conclusions from a wide variety of data sets.
Introduction to Statistics Course, which teaches the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing.
As discussed earlier, certain areas of mathematics form the foundation of AI algorithms. Linear algebra, calculus, probability, and differential equations are all mathematical tools that will be used in your AI journey.
Learn more about mathematics in the resources below:
Linear Algebra for Data Science in R Course, where you’ll cover the basics of linear algebra, including how to use matrix-vector equations, perform eigenvalue/eigenvector analyses, and PCA.
AI implementation requires a sound understanding of programming. Knowing how to write code allows you to develop AI algorithms, manipulate data, and use AI tools and libraries. Python is currently the most popular language in the AI community due to its simplicity, flexibility, and availability of data science libraries.
Python Programming Skill Track will help you improve your Python programming skills. You’ll learn how to optimize code, write functions and unit tests, and use software engineering best practices.
R Programming Skill Track, similarly, here you’ll level up your R programming skills by learning how to work with common data structures, optimize code, and write your own functions.
Data structures
Data structures allow you to store, retrieve, and efficiently manipulate data. Therefore, knowledge of data structures like arrays, trees, lists, and queues is essential for writing efficient code and developing complex AI algorithms.
Data manipulation involves cleaning, transforming, and manipulating data to prepare it for further analysis or feeding it into AI models. Skills in using libraries like pandas for data manipulation are essential for working in AI.
Data Manipulation with pandas Course teaches you how to manipulate DataFrames with pandas as you extract, filter, and transform real-world datasets for analysis.
Data science
Data Science is a blend of various tools, algorithms, and machine learning principles aimed at discovering hidden patterns from raw data. As an AI professional, understanding the process of extracting insights from data is crucial.
Machine Learning is a subfield of AI where machines learn from data to improve their performance or make accurate predictions. It’s essential to understand different machine learning algorithms, how they work, and when to use them.
Machine Learning Fundamentals in R Skill Track, where you’ll learn to predict categorical and numeric responses via classification and regression, and discover the hidden structure of datasets with unsupervised learning.
Machine Learning Cheat Sheet, which is a quick reference guide for the top machine learning algorithms, their advantages and disadvantages, and use-cases.
Deep learning
Deep Learning, a subset of machine learning, uses neural networks with multiple layers (hence ‘deep’) to model and understand complex patterns in datasets. It’s behind many of the most advanced AI applications today, from voice assistants to self-driving cars.
Deep Learning in Python Skill Track, where you’ll learn to use the powerful Keras, TensorFlow, and PyTorch libraries to create and optimize neural networks.
Deep Learning Tutorial, covering the most frequently asked questions about deep learning and explores various aspects of deep learning with real-life examples
Each of these skills interconnects with the others, helping you build a broad knowledge of AI concepts. A good way to start is to gain the foundations in each area before exploring the ones that interest you most in more detail. You can blend your approach as you see fit, working on the areas that naturally come up as you study and gain hands-on practice.
3. Learn the essential AI tools and packages
Knowing the right tools and packages is crucial to your success in AI. In particular, Python and R have emerged as the leading languages in the AI community due to their simplicity, flexibility, and the availability of robust libraries and frameworks. While you don’t need to learn both to succeed in AI, below you will find some key libraries and frameworks you need to get familiar with depending on the tool you end up choosing:
Top Python AI tools and packages
Python is a high-level, interpreted programming language known for its readability and versatility. It is widely used in AI thanks to its user-friendly syntax and the plethora of libraries and frameworks available for AI and data science.
pandas
pandas is a Python library that provides extensive means for data analysis. Data scientists use pandas for various tasks, including data cleaning, data transformation, and statistical analysis. It works well with incomplete, messy, and unlabeled data, making it a significant tool for preprocessing datasets.
NumPy, short for Numerical Python, is a library for Python that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. It’s an essential library for any scientific computation, including AI.
Scikit-Learn is a simple and efficient tool for data mining and machine learning. It is built on NumPy, SciPy, and matplotlib, and it’s open-source, meaning it’s freely available to everyone. It features various classification, regression, clustering, and dimensionality reduction algorithms.
PyCaret is a powerful Python library that simplifies the process of building and deploying artificial intelligence models, enabling users to efficiently explore, preprocess, train, tune, and compare multiple machine learning algorithms with just a few lines of code.
PyTorch is an open-source machine learning library based on the Torch library. It’s used for applications such as natural language processing and artificial neural networks. Its biggest advantage is its flexibility and speed, making it suitable for deep learning research.
Keras is a user-friendly neural network library written in Python. It’s built to minimize the time between your ideas and working models, offering a straightforward way for neural network modeling. Keras is also modular, making it incredibly versatile when constructing new models.
Once you’re ready to start getting hands-on with AI, utilizing APIs to access commercially available models is one of the best ways to get started. Commercially available APIs such as the OpenAI API, Cohere API, and Anthropic API are good places to start.
As your proficiency develops, explore pre-trained models using standard Python packages such as Hugging Face’s transformers and accelerate, which make it easy to utilize GPUs and TPUs.
Below, we’ve created a potential learning plan outlining where to focus your time and efforts if you’re just starting out with AI. Remember, the timescales, subject areas, and progress all depend on a wide range of variables. We want to make this plan as hands-on and practical as possible, which is why we’ve recommended projects you can work on as you progress.
Month 1-3: Basics of mathematics, programming, data structures and manipulation
Mathematics and statistics: Start with the basics of linear algebra, calculus, statistics, and probability. This will give you a strong foundation for what’s to come.
Programming: Learn Python, the most widely used language in AI. Start with the basics and then move to more advanced concepts. Take our Python Fundamentals Skill Track and Data Manipulation with Python Skill Track to cover the essentials, including packages like NumPy.
Data manipulation: Start learning about data manipulation and analysis. Get familiar with Python libraries like pandas and NumPy, which you’ll use for data manipulation. Learn how to clean and prepare data, which is a crucial part of any AI or machine learning project.
Month 4-6: Dive deeper into AI and machine learning
Basics of AI: Understand what AI is, its history, and its different branches. Courses like our AI Fundamentals can provide a good start.
Deepen your machine learning knowledge: Learn about different types of machine learning algorithms – supervised, unsupervised, semi-supervised, and reinforcement learning. Take our Machine Learning Scientist with Python Track, which covers the most important model types, model validation, and hyperparameter tuning. It covers packages including TensorFlow and Keras and touches on advanced deep learning.
Deep learning: Understand neural networks and deep learning.
MLOps basics: Learn about MLOps, which is about applying DevOps principles to machine learning systems. This includes model versioning, model deployment, monitoring, and orchestration.
Specialization: Based on your interests and career aspirations, specialize in one area – it could be natural language processing, computer vision, reinforcement learning, or any other field.
Stay Up-to-date: Regularly follow AI-related blogs, podcasts, and journals. Join communities to exchange ideas with other AI practitioners.
Ethics in AI: As you learn more about AI, be sure to also learn about the ethical considerations in AI.
Remember, this is just a basic roadmap. You should modify it based on your pace and interests. AI is a vast field, and learning is a continuous process.
Top 5 Tips For Learning AI
Embarking on the journey to learn AI can be daunting, but having a strategic approach can make the process more structured and manageable. Here are five steps to guide you through your AI learning journey:
1. Choose your focus
Start by deciding where to focus your efforts based on your career goals. We discuss the various careers in AI further in this article; each role has a different focus and requires a unique skill set.
For example, if you’re interested in a more applied role like a data scientist or machine learning engineer, focus more on programming, data science, and understanding various machine learning algorithms. Your aim here is to become proficient in using AI techniques to solve real-world problems.
Alternatively, if you’re leaning towards a research role, you should delve deeper into the theory behind AI and machine learning. You’ll need a solid grasp of mathematics, statistics, and theoretical computer science.
This is not a rigid distinction; rather, it’s a starting point to help you decide where to put your initial focus.
2. Start learning
Once you’ve decided on your focus, it’s time to start learning. The learning resources suggested in the skills section and the AI learning plan above are useful places to start. Remember, mastering AI is a marathon, not a sprint. Take your time to understand each concept thoroughly before moving on to the next one.
3. Apply your skills to projects
There’s no better way to learn than by doing. Applying the skills you learn in real-world projects solidifies your understanding and gives you practical experience that can enrich your portfolio. This could be as simple as creating a machine learning model to predict house prices or as complex as developing a deep learning model for image recognition. We’ve included example projects throughout this article.
4. Join a community
Join AI communities, both online and offline. Participating in forums like Stack Overflow or GitHub, joining AI groups on LinkedIn, or attending AI meetups and conferences can offer invaluable learning opportunities.
Once you’ve earned a DataCamp certification, you can join our DC Certified Community, where you can connect with other certified learners, access exclusive content and events, and make the most of your newly acquired skills.
By joining communities, you can stay updated with the latest trends, get help when you’re stuck, and network with other AI enthusiasts.
5. Keep iterating
AI is a rapidly evolving field. Once you get the basics, it’s important to keep learning and improving your skills. Follow AI blogs, read research papers, take advanced courses, and always be on the lookout for new ways to challenge yourself. This iterative process will turn you from a novice into an expert.
Remember, the journey to learning AI is challenging but immensely rewarding. Don’t get discouraged if you encounter obstacles along the way; they’re part of the learning process. Keep your end goal in mind, and stay committed to your journey.
The Best Ways to Learn AI
A rich abundance of resources is available to help you learn AI, from structured courses and textbooks to hands-on projects. Choosing the right resource can help you get off to a strong start with your AI learning. Here are our top picks to get you started on your AI learning journey:
Take the top AI courses to get started
DataCamp is a great platform that offers interactive courses specifically tailored for aspiring data scientists. The courses range from beginner to advanced levels and are designed with hands-on exercises. Here are some of the best AI-related courses on DataCamp:
Learning AI is a journey that goes beyond understanding theories and coding techniques. It involves hands-on practice, and this is where completing AI projects comes in.
Start by identifying a suitable project based on your proficiency level, be it a predictive model or a generative AI application. The right tools, such as Python and its extensive libraries, will be your cornerstone. Understanding and preparing your data is crucial, as it directly impacts your model’s effectiveness.
Naïve Bees: Image Loading and Processing. Work with image data, build classifiers using traditional techniques and leverage the power of deep learning for computer vision.
Remember, the key to learning AI is consistency and practice. Don’t be afraid to start small and gradually work your way up to more complex concepts and projects. You’ll be amazed at how much you can learn by dedicating just a little time each day to studying AI.
Check out the best AI cheat sheets
Our selection of data science cheat sheets can act as a quick reference guide on a wide variety of topics, including many related to AI:
Artificial intelligence has opened up numerous career paths, each with unique responsibilities, tools, and required skills. Let’s delve into three AI careers that are popular at the moment: data scientists, machine learning engineers, and research scientists. Note that we will be mentioning some of the tools they need to master; if you are not familiar with these tools, that’s completely okay—we’ll be covering them in more detail in a later section.
Data scientist
Data Scientists investigate, extract, and report meaningful insights into an organization’s data. They communicate these insights to non-technical stakeholders and have a good understanding of machine learning workflows and how to tie them back to business applications. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools.
Data Scientists are the detectives of the data world, responsible for unearthing and interpreting rich data sources, managing large amounts of data, and merging data points to identify trends. They utilize their analytical, statistical, and programming skills to collect, analyze, and interpret large datasets. They then use this information to develop data-driven solutions to challenging business problems. A part of these solutions is developing machine learning algorithms that generate new insights (e.g., identifying customer segments), automate business processes (e.g., credit score prediction), or provide customers with new-found value (e.g., recommender systems).
Key skills:
Strong knowledge of Python, R, and SQL
Understanding of Machine Learning and AI concepts
Proficiency in statistical analysis, quantitative analytics, and predictive modeling
Data visualization and reporting techniques
Effective communication and presentation skills
Essential tools:
Data analysis tools (e.g., Pandas, NumPy)
Machine learning libraries (e.g., Scikit-learn)
Data visualization tools (e.g., Matplotlib, Tableau)
Big data frameworks (e.g., Airflow, Spark)
Command line tools (e.g., Git, Bash)
Machine learning engineer
Machine learning engineers are the architects of the AI world. They design and deploy machine learning systems that make predictions from organizations’ data. They also solve problems like predicting customer churn and lifetime value and
are responsible for deploying models for the organization to use. Machine learning engineers usually work exclusively with coding-based tools.
Key Skills:
Deep knowledge of Python, Java, and Scala
Familiarity with machine learning frameworks (like Scikit-learn, Keras, or PyTorch)
Understanding of data structures, data modeling, and software architecture
Ability to work in a team and exceptional problem-solving skills
Tools:
Machine learning libraries and algorithms (e.g., Scikit-learn, TensorFlow)
Data science libraries (e.g., Pandas, NumPy)
Cloud platforms (e.g., AWS, Google Cloud Platform)
Version control systems (e.g., Git)
Research scientists
Research scientists are the thinkers of the AI world. They conduct cutting-edge research to advance the state-of-the-art in AI. Their work often involves inventing new algorithms or improving existing ones. They also present their findings at AI conferences and in scholarly articles.
Key Skills:
Solid understanding of machine learning and deep learning
Proficiency in Python and other programming languages
Extensive knowledge of AI-related mathematical theory (like statistical learning theory)
Ability to conceptualize and validate novel AI models
Strong writing and public speaking skills
Tools:
Deep learning frameworks (like TensorFlow, PyTorch)
Scikit-learn, TensorFlow, Pandas, NumPy, AWS, Google Cloud Platform, Git
TensorFlow, PyTorch, MatLab, Mathematica, LaTeX, Google Slides, AWS, Google Cloud Platform
Each of these careers offers a unique path into the world of AI. They all hold tremendous potential and serve critical roles in the field. Your decision will depend on your interests, strengths, and long-term career goals.
How to Find a Job in AI
A degree can be a great asset when starting a career in AI, but it’s not the only pathway. While we do think it’s worthwhile to get a formal education in AI or an adjacent field, more and more professionals are entering the field through non-traditional routes, proving that with dedication, consistent learning, and a proactive approach, you can land your dream job in AI.
Here’s how to find a job in AI without a degree:
Keep learning about the field
Stay updated with the latest developments in AI. Follow influential AI professionals on Twitter, read AI research papers, and listen to AI-related podcasts, including DataCamp’s own DataFramed Podcast.
Some of the AI thought leaders to follow include Yoshua Bengio, Fei-Fei Li, and others. You’ll gain insights into trending topics, emerging technologies, and the future direction of AI.
A strong portfolio that showcases your skills and projects can make you stand out from other candidates. More importantly, a project attempting to solve a real-world problem will impress hiring managers.
The key to standing out is to show your project made an impact and show that other people cared. Why are we in data? We’re trying to find insights that actually impact a business, or we’re trying to find insights that will actually shape society or create something novel. We’re trying to improve profitability or improve people’s lives using and analyzing data, so if you don’t somehow quantify the impact, then you are lacking impact.
Nick Singh, Co-author of Ace the Data Science Interview
Your portfolio should contain a variety of projects that highlight your proficiency in key AI tools and algorithms. Check out our article on how to build a great data science portfolio for more information and tips.
Develop an effective resume
In today’s digital age, your resume isn’t just read by human eyes; it must also pass through Applicant Tracking Systems (ATS). These are automated software used by many companies to filter out resumes that don’t meet certain criteria. Consequently, it’s crucial to tailor your resume to be ATS-friendly while still catching the eye of hiring managers.
According to Jen Bricker, former Head of Career Services at DataCamp:
60% to 70% of applications get shifted out of consideration before humans actually look at the application.
Jen Bricker, Former Head of Career Services at DataCamp
As such, it’s extremely important to structure resumes as effectively as possible. You can read more about building a stand-out data scientist resume in a separate article.
Get noticed by hiring managers
Being proactive on social platforms can help attract the attention of hiring managers. As Sadie St. Lawrence, CEO of Women in Data, points out on the DataFramed Careers Series, sharing your projects and ideas on LinkedIn or Twitter, engaging with AI communities, and contributing to open-source projects can increase your visibility and showcase your passion for AI.
If you want to be seen, then you have to share your work. The analogy I like to use is a music box. If you’ve ever seen a music box, when it’s closed and just sitting on the table, you never actually get to hear what the beautiful sound is inside of it. It’s similar for data scientists that lack communication skills. They may have these amazing skills, but they’re all locked in this box, and no one ever knows about them. You have to open the box, and you do that by being able to tell those stories and communicate those skills. So, it’s really up to you. Do you want people to hear your story and experience your amazing skills and ability? Then you’re going to need the communication skills so that you can open your box.
Sadie St. Lawrence, CEO of Women in Data
Remember, breaking into AI takes persistence, continuous learning, and patience. But with these steps, you’re setting yourself up for success. Good luck with your AI journey!
How to Master AI Tools for Business
While this article has primarily focused on an in-depth understanding of AI for aspiring AI practitioners, it’s equally crucial for business professionals to familiarize themselves with AI tools. Generative AI tools like ChatGPT are increasingly becoming integral to various business operations.
Moreover, as Noelle Silver, Global AI Solutions & Generative AI & LLM Industry Lead at Accenture, mentioned on the DataFramed podcast on how organizations can leverage AI, Large Language Models are becoming increasingly embedded in most of the software interfaces professionals use on a daily basis.
Large Language Models like ChatGPT are revolutionizing the way we interact with software. Whether it’s customer service, project management, or data analysis, these AI tools are enhancing efficiency, accuracy, and productivity across all sectors.
Noelle Silver Russel, Global AI Solutions & Generative AI & LLM Industry Lead at Accenture
Learn more about generative AI
Firstly, get acquainted with the different AI tools available to you. There’s a broad spectrum of tools designed for various business functions, such as marketing, sales, customer service, and data analysis. Explore the AI tool landscape and understand the capabilities of each tool. How can they benefit your role or business?
For instance, consider our Introduction to ChatGPT course, which offers a comprehensive understanding of this powerful generative AI tool. We also have an introduction to Generative AI Concepts course which covers a rang of other tools as well.
Apply generative AI tools at work
Once you understand the AI tools, the next step is to apply them in your daily workflows. Whether you want to automate data analysis, improve customer interactions, or streamline business processes, AI tools can benefit you, your teams, and the wider organization.
We’ve compiled several resources that provide practical guidance on how to use AI tools effectively in your work:
While AI tools are incredibly useful, they’re not infallible. It’s important to review and edit the outputs generated by these tools. Understand their limitations and adjust their use accordingly. Remember, these tools are designed to assist you, not replace your decision-making. Understanding the ethics of AI is essential.
Mastering AI tools in business aren’t just about understanding the technology; it’s about knowing how to leverage them effectively to drive success in your role and business. By learning, applying, and refining their use, you can stay ahead of the curve in the AI-driven business world.
Conclusion
Learning AI is a rewarding pursuit that opens doors to a world of innovative technologies and exciting career opportunities. The knowledge and expertise gained through this process go beyond textbooks and lectures. It involves a dynamic cycle of learning, applying, experimenting, and improving. Embracing a hands-on approach, particularly through courses and AI projects, accelerates learning and cultivates essential skills in problem-solving, critical thinking, and creativity.
If you’re just starting out on your AI learning journey, we’ve outlined loads of helpful resources to help you get started, as well as an example learning plan for some of the key themes you’ll need to master on your way to become proficient in artificial intelligence.
