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How to learn Machine Learning and Artificial Intelligence?

How to learn Machine Learning and Artificial Intelligence? In this article, I am going to acknowledge you with some of the renowned and useful resources that are really handy for you. I have extracted these resources from many blogs and videos by experts of this domain. But, most of its section contains resources that I have been practicing so far. This is really important as ML enthusiasts get overwhelmed by enormous and huge list of articles, blogs and videos available on web. So, find your best suitable companion out of this fruitful bucket and get started with this beautiful journey. For any doubts and queries, feel Free to ask in comment section.
Submitted by Atul Anand, on June 19, 2018

STRATEGY

  • Learn theory + practical aspects.
    (At first get an overview of what you are going to learn).
  • Gain a good hold/insight on each concept.
  • If you are not comfortable with maths at first; just get yourself comfortable with why we needed that maths part, and what is its O/P. Then, come to understand it later. Never skip any concept forever.
  • PRACTICE, PRACTICE, PRACTICE and PRACTICE!!!
    (Coding comes into this phase)
  • Understand boundary cases and failure concepts, to grap the concept of that topic.
  • BELIEVE! Its easy;
    Total of 150+ hours is good enough (5-10 hrs for 3-6 months)

SPECIAL TIPS

  • For those facing difficulty in maths (like me :))
    You need to consider math as Poetry + Art.
  • Eqns :- Read in English sentence → Poetry
  • Geometry :-Visualize (human–visualizing creature) → Art

Steps and Guidelines

You should note that, this is not the only way to approach for learning ML/DL. But this is really one of the best resource list for ML. You may have an option to pursue any certification course of your choice. It’s also good. I don’t discourage you for that. But, In case you want to save your money or you want to give ML a try and don’t want your money wasted in case you can’t continue. Then, you must follow some free available stuff online. And, trust me; you can never get a list better than this one. I have narrowed everything so precise so that you don’t get distracted elsewhere.

1) Programming language (Python or R)

Book Think python; ‘O’ reilly - Publication
Book Learn python the Hard way; Zeads Hald
Site www.Guru99.com/Python 3
Site Python Tutorial, Tutorialspoint

2) Probability and statistics

Online course Statistics & probability, Khan Academy
Blog Basics of statistics for machine learning engineers I + II - -Joydeep Bhattacharjee
Slideshare Probability basics for Machine learning (CSC2516) - Shenlong Wang*

3) Linear Algebra

Online course - Linear Algebras; Khan Academy

4) Calculus & Numeric Optimization

Online course Multivariable calculus, Khan Academy
pdf Derivatives, Back propagation and vectorization; Justin Johnson
pdf Vectors, matrix and Tensor derivatives; Erik–learned Miller

5) Brief of Machine learning

Book what you need to know about machine learning - (Packt publication) – Gabriel A. Canepa
YouTube Intro topics for Machine Learning – UB Vzard
Blog Analyticsvidhya

Note: At this stage, I would like to personally recommend you a free available online course: Machine Learning @ Kaggle | Learn
- This will give you a basic to intermediate level of understanding in ML. Plus; you would learn How to compete at different platform like Kaggle, or Hackerearth.

6) Classification and Regression technique

Online course Machine Learning, Andrew Ng; Course era/ YouTube
YouTube Classification Techniques; UB Vzard
YouTube Regression Techniques; UB Vzard
Blog Analyticsvidhya

7) Clustering Techniques

Same as above (6)
YouTube - Clustering techniques; UB Vzard

8) Dimensionality Reduction

Same as above
YouTube - Dimensionality Reduction Techniques; UB Vzard

9) Neural networks and deep learning

Online courses

  • Deep learning; Kaggle | Learn; Dan.S.Becker
  • Deep Learning, Andrew Ng; Course era/YouTube
  • Convolution Neural Networks; Stanford online/ YouTube (CS231n) (*If you want specifically CNN at broader scale.)
  • Deep Learning A-ZTM; Udemy
  • U B Vzard

10) Problem solving

  • Kaggle.com - solve problems end to end
  • Hackerearth.com - Participate in contests
  • Analyticsvidhya.com - compete in Data Hacks and Student Data fest

Understand why a technique is working (or) not working

  • Document /code (GitHub or blog)
  • Portfolio of 5 or more case studies
  • Read other’s blog or code

11) Youtube series – UB Vzard

12) LinkedIn – Get in touch with Data Science community professionals. They will Help you, guide you and most importantly motivate you.

Note: Of course; this awesome article series @ IncludeHelp. Stay tuned for totally aligned and simplest platform for insightful knowledge at ease.

Conclusion

At last, I would like to conclude that, don’t waste your crucial time wasting behind finding learning resources; although this is important before getting started. This bucket is really helpful and good enough to get you from Beginner to Advance Level. Find and mark out the best one and most suitable for you. And start over as soon as possible. And, always stick to that. You can take references from other resources too. A hearty apology, because U B Vzard is active on YouTube but it has not contained any ML videos yet; But, I am working on it with a leopard speed. You will have them ASAP. Don’t lose your hope. Trust me, it is easy. Catch you later in the next article. HAPPY LEARNING!






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