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Ultimate Guide on How to Learn AI in 2023

In this Ultimate Guide we will cover all the necessary steps to become advanced in AI. You will discover the best outline with actionable steps to learn Artificial Intelligence, It will also cover all the best tricks and tips to learning AI, and valuable resources to learn from.

What is Artificial Intelligence?

AI is all about building machines or systems that can process information just like humans do.

It's exploring ways to give computers the potential to learn, make decisions, recognize patterns and more - tasks that usually require human intelligence.

In short, it's about making machines intelligent. AI is used in a wide range of fields, from healthcare to education, finance, transportation and more.

Why learn Artificial Intelligence?

Develop Exciting New Applications: AI technologies have the potential to revolutionize the way we think about technology, providing the opportunity for different industries to develop exciting new applications.

Learning AI offers a lot of great opportunities. Not only is it a great path to a bright career as companies start to need more and more AI professionals, but it also allows us to solve complex problems and to develop new applications!

Healthcare, finance, transport, education - all these industries, and many more, are benefiting from AI and its potential to analyze huge swaths of data and find hidden patterns for better decision-making and efficiency. And that's not all!

AI technologies have already revolutionized the way we approach technology – so there's tremendous potential to explore and create novel applications, too.


If you want to learn AI, It would be helpful to have strong foundation in mathematics, computer science, and coding, because when you want to actually build the AI It is all code.

Also would be helpful if you have some understanding of topics like statistics, algorithms and data structure these especially can help you when you create AI.

3 phases of learning AI


Learning about AI starts with getting to know the basics. This means understanding the different types, the algorithms used, and the tools/platforms accessible to create with AI. It can be a little overwhelming but with a little research and help you can quickly start to understand the underlying fundamentals of AI.

Fundamental topics:

  1. Learn the basics of programming (python)

  2. Understand the mathematics behind AI

  3. Learn the basics of machine learning

  4. Familiarize yourself with deep learning

  5. Practice with AI tools and frameworks

Machine Learning

The second phase of learning AI is to dive into machine learning which is a subset of AI. You need to dive into the machine learning topics this is necessary if you want to build AI because for example if you don't know how to prepare data to learn the model how can you build one?

Machine learning topics:

  1. Data preparation

  2. Supervised learning

  3. Unsupervised learning

  4. Reinforcement learning

  5. Evaluation metrics

  6. Overfitting and underfitting

  7. Model selection and hyperparameter tuning

AI Models

The third phase of learning AI is understanding the biggest AI models in a particular area of AI, such as computer vision, Deep learning, or natural language processing. When you understand how these models work It is then a lot easier to build your own AI models.

Biggest AI models:

  1. Deep learning

  2. Convolutional neural networks (CNNs)

  3. Recurrent neural networks (RNNs)

  4. Transfer learning

  5. Natural language processing (NLP)

  6. Computer vision

Resources to learn from

Online Courses

1. Introduction to Artificial Intelligence with Python - edX (IBM)

This course is a great way to get started with Artificial Intelligence. It covers Python, the one of most used programming languages behind AI.

It explores machine learning, natural language processing, and robotics, also a bunch of other topics too.

2. Deep Learning Specialization - Coursera

This course covers the deeper corners of deep learning: like convolutional neural networks and recurrent neural networks.

But it also talks about TensorFlow; that's the most popular deep learning framework. It's a must-know if you really want to understand DL.

3. Machine Learning - Coursera (Stanford University)

This course by Andrew Ng is an awesome machine learning course. It covers the important topics such as linear regression, logistic regression and neural networks.

Best part, the course is self-paced and all the materials are free!

4. AI for Everyone: Master the Basics - Coursera

This course is for non-techy people who want to learn about AI. It covers stuff like supervised learning, unsupervised learning and deep learning.

5. Reinforcement Learning - Udacity

This course offers an introduction to reinforcement learning - a type of machine learning that's used in robotic development and game-making.

It'll take through topics like Q-learnin' and policy gradients. So if you're looking to get up to speed with this really cool field of AI, then this course is for you.


For more resources check this article in our community that covers all the best resources to learn Artificial Intelligence.

Participate in AI projects

Participating in AI projects can be a great way to get some real hands-on experience with artificial intelligence.

Kaggle and GitHub are some places you can find AI projects. When deciding on a project, you probably want to make sure that it interests you and is aligned with your goals of learning.

Additionally, you should make sure to consider current needs, such as projects involving computer vision, natural language processing and more.

Some of the staples in AI are stuff like image classification, object detection, speech recognition and sentiment analysis.

Create your AI

Creating actual AI model is one of the best methods how to learn AI, because you actually create the AI and don't only learn about It, when you create AI you also learn coding a lot.

If you don't know how to create AI you have more that 2 options, but I will outline some possible options how to create AI if you don't know how to code:

  1. Use existing AI models ChatGPT that can generate code for you and this Is especially helpful if you are creative, but I requires less time that building It your self, but It is not instant you will need to twak the code and repair It and this can take some time.

  2. Use Free Guiding Tool like ours. It helps you setup specific models with step-by-step instructions that everyone can follow.

Build a Network in AI

Connecting with other AI professionals and attending events and conferences can help you stay ahead of the curve in artificial intelligence.

Grow your knowledge by hopping onto online groups and forums like ours, attending AI-related gatherings, and becoming part of open-source AI initiatives. This way you can build a strong AI network.

Great place where to build your network is our Discuss AI forum, where you are free to ask, answer and share knowledge about AI.


Learning AI is not easy, but those who do master it are rewarded greatly. To reach that point you need to go through three phases - getting the prior knowledge, looking for the right resources, working on projects, making your own AI and networking. After you do that, you can become a skilled AI expert and benefit from plentiful job offers in the sphere. Don't wait any longer - take on the challenge of AI and see what it has to offer you!


Related Questions

Can I learn AI on my own?

Yes, you can definitely learn about AI online through online courses, books, and other resources can be done, but it needs tons of dedication, effort and also commitment. The field of AI is growing and changing so quickly, so staying on top of it is crucial if you want to get the most out of your self-study. It's going to take a huge commitment but you can do it!

Is AI difficult to learn?

AI is a complex field. Learning it can be quite challenging especially if you are not familiar with programming or statistics and many other things we discussed before. However, with persistence and hard work, anyone can learn AI.

Can I learn AI in 3 months?

It depends on your prior knowledge, skills, and learning speed. If you have a strong foundation in programming, mathematics, and statistics, and are willing to put in the effort, you may be able to gain a basic understanding of AI in 3 months.

Does AI require coding?

Yes, AI requires coding skills in languages such as Python, R, Java, and C++. However, you do not need to be an expert programmer to learn AI. Basic coding skills are sufficient to start with.

Can a non IT person learn AI?

Yes, a non-IT person can learn AI. However, it may take more effort and time to understand the technical concepts and programming skills required for AI. It is recommended to start with the basics of programming and mathematics before diving into AI.

Can I learn AI for free?

Yes, there are many free resources available online, such as online courses, tutorials, and forums, that can help you learn AI. If you want to know the best Free resources to learn AI check out this article. When It comes to paid courses and certifications may offer more structured and in-depth learning.

Is AI a lot of math?

Yes, AI involves a lot of mathematics, including linear algebra, calculus, and probability theory. However, you do not need to be a math genius to learn AI. Basic math skills are sufficient to start with.

Which AI language is easiest?

The easiest AI programming language is Python, It is considered the easiest language to learn because It is relatively simple to learn compared to other AI programming languages.

Is studying AI easy?

Studying AI can be tough, but it comes with its own rewards. It definitely calls for dedication, hard work and the desire to keep learning. But with some help from the right resources and support, anyone can tackle AI.

Is studying AI worth it?

Studying AI is definitely worth it - it's one of the fields with highest demand in the job market at the moment and has an excellent future outlook. AI could totally transform industries such as healthcare and finance, plus it opens up new possibilities for innovation and growth. Can't see any reason why you wouldn't want to explore it!

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