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How to Learn AI

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By: Happi Yvan

March 5, 2021

Artificial Intelligence is one of the fastest-growing fields alongside its friend Cybersecurity. The immense data available, structured and unstructured, and the need for machines to understand, process, and use data effectively with little to no effort provided by humans accelerates the need for AI practitioners and experts.

Interest and investment in AI emerged rapidly when machine learning was successfully applied to numerous problems in academia and industry due to the application of powerful computer software and hardware, new methods, and immense data collection sets.

AI is a pretty exciting and fun area of study. Skills in this domain are one of the most sought-after in the IT world. Many companies, organizations, firms, and startups are willing to invest heavily to have their businesses exploit what AI has to offer.

Learning AI can be demanding. Getting your objectives right is the first and foremost thing to do. It's a pretty long journey to begin with. It depends heavily on other fields like mathematics, computer science, psychology, linguistics, etc., depending on the domain of interest, be it Natural language processing, Machine Learning, Computer Vision, Robotics, etc. Thus, it's very important that you make your target very clear. The following questions should help achieve the right objectives and priorities:

  1. Why do you choose to pursue this field of study?
  2. What's the driving force or motivation behind your interest and pursuit?
  3. What are you willing or hoping to do with the skills once obtained? That is, what problems are you willing to solve using the skills obtained in this field of study?


Artificial Intelligence is a broad field that originated in the late 1900s and is relevant to any intellectual task. Having a foundational understanding of 1 the primary goals and objectives and 2 approaches with respect to schools of thought and the various application areas of AI is vital and broadens the view (at the very least) of motivations and thinking behind AI before engaging with significant time investment for studies into this field.

LEARNING WITH MOOC (Massive Open Online Courses)

With the above in perspective, following up with a MOOC is beneficial and will make the learning process more exciting since the courses have been tailored with multiple audiences in mind. So, the material is well structured and has links to external resources for further understanding. Some platforms provide workspaces, which are virtual environments (sometimes called virtual desktops) already set up to develop projects or exercises directly from their platform. This helps speed up the learning process.


Despite what may be spreading around about the "You don't need to learn AI," if anyone is going to be doing some serious AI, he/she will need a solid math base to learn AI and handle the challenges. A common mistake people make while learning AI is running or shying from mathematics. Math is an absolute necessity in learning AI.


In the real world, the application of AI will demand some considerable programming. Testing out theories or building sample AI models will need some computing muscles. For starters/beginners, learning Python as the first (or as a go-to) language for AI will make the practitioner's learning curve smooth. Most online tutorials make extensive use of Python as the primary language for AI development. Most MOOCs use Python as a standard, and others are shifting towards Python as the primary language when dealing with computing in AI. This is partly because of the simplicity, readability, and maintainability of Python. It's easy to learn. Specialization from Michigan University like Python 3 Programming has a gentle and interactive introduction to Python 3 programming.


It is no surprise that Artificial Intelligence makes use of a lot of algorithms. Some are pretty straight forward, and others are complex in design. Algorithms are at the heart of Artificial Intelligence. Having a profound knowledge and understanding of algorithms will greatly help appreciate the beauty and depth of most AI domains like Machine Learning, Computer Vision, Natural Language Processing, and Facial Recognition. This is not to say other disciplines don't use algorithms; these are the hot AI domains of today.

Some resources that will help with getting hands-on experience with algorithms, understanding them, and preparing one to design his/her algorithms.


Believing that the AI Enthusiasts have a black belt in the above fundamentals, he/she should focus on an area (or areas) of interest. This could be Machine Learning, Neural Networks, Robotics, Expert Systems, Fuzzy Logic, Natural Language Processing, or any other field. Learning all the fields may seem impossible since each field alone goes into considerable depth, and many researchers have spent decades exploring a particular field. There are still alot of problems to solve and much to uncover. At this stage, laser focus is needed on a specific domain since the essentials have been obtained.


  1. AI Goals
  2. AI Approaches
  3. AI Applications
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