AI has already started to change the world we live in. We now have access to artificial intelligence tools that are making certain areas of work and life faster and more productive. The pace of change is startling, making many more people want to learn AI.
Whether you want to become a data scientist, a machine learning engineer, an AI researcher, or you're simply an AI enthusiast, this guide is for you. We'll cover how to learn AI from scratch and provide practical advice and tips from industry experts to help your learning journey. As well as covering the skills and tools you need to master, we'll also explore how businesses can use AI to be more productive.
If you're short on time and want to know how to learn AI from scratch, check out our quick summary. Remember, learning AI takes time, but with the right plan, you can progress efficiently:
Months 1-3: Build foundational skills in Python, math (linear algebra, probability, and statistics), and data manipulation.
Months 4-6: Learn core AI concepts, including machine learning algorithms, model building, and deep learning basics.
Months 7-9: Specialize in areas like NLP, computer vision, or AI for business. Work on real-world projects.
Months 10+: Keep improving! Follow AI research, contribute to projects, and explore advanced topics like AI ethics and MLOps.
The rest of this guide provides the best resources, expert insights, and a structured plan to take you from beginner to AI practitioner in under a year.
Learn the fundamentals of AI and ChatGPT from scratch.
You'll find that AI technology is discussed in various ways, with various acronyms and phrases. To help simplify the remainder of the article, it’s important to look at the main 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 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.
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 these terms:
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.
Artificial Intelligence is more than just a buzzword; it's a revolutionary technology changing 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 in so many fields. There's no better time than now to start learning AI. Here's why:
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.
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.
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 2025:
Succeeding in AI requires mastery of three critical areas:
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.
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 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:
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:
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 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.
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.
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.
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:
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 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.
Llama (Large Language Model Meta AI) is a family of open-source LLMs developed by Meta (formerly Facebook). It provides a powerful alternative to proprietary models like GPT-4o and Claude Sonnet, allowing researchers and developers to fine-tune and deploy AI models efficiently.
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.
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.
5. Deepen your machine learning knowledge: Learn about different types of machine learning algorithms
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
Specialize further: Depedening on career paths
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.
Below, we've compiled the info from the learning path to visualize how to learn AI in 2025:
AI Learning RoadmapEmbarking 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:
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.
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.
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.
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.
By joining communities, you can stay updated with the latest trends, get help when you're stuck, and network with other AI enthusiasts.
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.
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:
