Machine Learning: road map for beginners

Thứ hai - 17/08/2020 05:06
Machine Learning: road map for beginners

There is no doubt that Machine Learning is one of the hottest subjects today, and there are more and more people who want to learn about it. So I would like to create this post which aims at providing a road map for complete beginners who want to start their career in Machine Learning. If you’re already an expert, just skip this.

In fact, today there are a number of libraries and frameworks such as sci-kit-learn, Keras, or TensorFlow which allow you to build a predictive model in just some lines of code, but if you don’t understand the algorithms’ principlesyou may get stuck in choosing the right models and the right parameters, which may waste you a lot of time. For example, Normal Equation can help to find the best fitting hyper-plane in Linear Regression but why in many cases we should use some gradient-descent based methods? Or when using gradient descent, what is the impact of the learning rate? Or in Neural Network, is adding more layers always better? Why do we need to randomly initialize weights before training network, and how they should be generated? 

FIRST STEP: Prepare a mathematical background!

If someone says that he wants to start learning ML without math, he does not seriously want to do it!
Indeed, you don’t have to be a mathematician to learn about ML, but some basic background in Linear Algebra, Calculus, and Probability would help you learn ML as fast as a rocket.

SECOND STEP: Prepare a basic coding skill

There is no slot for non-coder who wants to be a specialist in ML. Otherwise, my mom would apply for one!

If you already have a solid coding background, then congratulation again. But if you do not have it yet, just pick a programming language to learn. As the matter of fact, Python is emerging as the most suitable language for learning and for doing ML, but depending on your specific projects, you may want to choose C++, Java, or anything else. 

THIRD STEP: Take a Machine Learning course

This should be an ML course from scratch which teaches you step-by-step the fundamentals of ML, rather than the ones which teach you how to use just three lines of code to build an ML model! Those courses will be for a later step, not now. Concretely, you will be walked through the most important algorithms with practical exercises on real-world problems using Python. Moreover, it also covers other AI’s branches such as Fuzzy Logic and Evolutionary Computation, which will be very useful for your study and research on AI.

FOURTH STEP: Take a more advanced ML course

Once you’ve got the fundamentals of ML, the next step is to find some more advanced courses which focus on Deep Neural Network, Convolutional Neural Network, and Sequence Models. You can also look for some special ML courses that focus on your specific problems such as Computer Vision, Natural Language Processing, or Data Science …

FIFTH STEP: Take an Applied Machine Learning course

Now you can look for some kind of hand-on courses that teach you how to use the most powerful ML libraries and frameworks such as sci-kit-learn or TensorFlow which allow you to build ML models in just some lines of code. Finding an ML book is also a good choice.

SIXTH STEP: Practice real-world projects

It seems not to be easy to get a job without any year of experience, so you have to build your own. Kaggle is an ideal place to test your ML skills. Join it, try to play with some data sets, and challenge yourself by joining into some competitions. Then you can propose some freelancing works in order to build your own experiences. However, it would be always better if you could find an ML job after all.


So, above is the 7-step road map for complete beginners who want to start their career in Machine Learning, according to my opinion. You may want to propose your own way, of course.
If you start from zero, it may take you one year from step 1 to step 5, in order to get the basic level. The sixth step should be taken during at least one year then you will get into the intermediate level. Finally, to become an expert in this domain, it will not take you less than three years. That means, from zero to hero (expert), be prepared for the next five years or more, except you are a Mensa’s member!

 Tags: AI, ML:, DS

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