Absolute Beginner’s Guide to Machine Learning and Deep Learning

What Time Is it ⏰?

I think the first thing you need to consider to start learning Machine Learning is, how much time you have to spare. If you have 3–4 month’s in your sleeve, your schedule, and learning outcome will be significantly different from someone who has 1 month or less. My suggestion will be to take your time and absorb it slowly. Take 4–5 months if necessary (more if that’s necessary too) to learn the basics of Machine Learning well. Don’t try to rush it in. It’s important to learn the basics right. It’s easy to become tool dependent in Machine Learning without actually knowing what’s happening inside. But remember, the tools are always changing. Every month there is a new deep learning technique that is better than the previous one. Whatever tool you learn will certainly become obsolete at some point, but the basics will remain the same. So that’s what you should be focusing on. But if you don’t have that time, I’ve created a crash course for you at the end of this blog.

4~5 Months:

Part 1: Start With Machine Learning, 2 months

My absolute favorite way start Machine Learning is the Coursera Machine Learning course by Stanford University. This course is taught by the legend Andrew Ng himself. It’s a bit old, and *slightly* outdated, but I’ve seen a lot of sources on the Internet, and nothing I’ve found is anywhere close to this one. So, if you have time for a 14 week commitment, definitely do it. But if you are in real real hurry, you can do Week 1 to 5, and skip the rest.

Neural Network Playlist

Part 2: Deep Learning, Here I come (1 month)

Before you start Deep Learning, you need to brush up some university math. The Deep Learning Book by Ian Goodfellow (another legend of Deep Learning) sums up most of the important topic very concisely. I recommend you go through Linear Algebra and Probability and Information Theory chapters as deeply as you can. Whenever you get stuck with any Deep Learning concept while reading a paper, you can come back to this book for reference. If you need a PDF for this book, you can get one here.

Part 3: Practical Implementation of Deep Learning (1~2 months)

So far you should have a proper understanding of most of the concepts in Machine Learning and Deep Learning. Now it’s time to get real.

2 Months or Less:

  1. Complete the first 5 weeks of the Machine Learning course from Coursera. Do the programming exercises.
  2. Watch the Neural Network playlist from 3Blue1Brown YouTube channel.
  3. Complete Course 1 (Neural Networks and Deep Learning) from Deep Learning Specialization in Coursera. Do the exercises.
  4. If you want to start an Image Processing project, take the 4th course in Coursera specialization, or if you want to work on Natural Language Processing or sequence data, take course 5.
  5. Search for open source implementation and YouTube videos of projects that you are interested in. If you are concerned about which language to use, I think it’s good to stay with Keras (with Tensorflow backend) for a while. Later you can move to Tensorflow or PyTorch, depending on your needs.

1 Month or Less:

It will be ridiculously hard to sum up the Deep Learning resources in a 1 month schedule. But if you just need an idea of how Machine Learning works, and then apply it to your project, here’s my best guess what you should do.

  1. Skim through Coursera Machine Learning course Week 1 to 5. Just watch the videos, grasp the concept. You can skip the MATLAB/Octave tutorials in Week 3.
  2. Watch the Neural Network playlist from 3Blue1Brown YouTube channel.
  3. Skim through Course 1 (Neural Networks and Deep Learning) from Deep Learning Specialization in Coursera.
  4. If you want to do an Image Processing project, read the chapter 6 from Nielsen’s book: http://neuralnetworksanddeeplearning.com/chap6.html
    Or if you need some idea about Sequence Modeling, head over here to Olah’s blog: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
  5. Siraj Raval has some interesting video’s to give you a gist of most ML and DL topics.
  6. Search for open source implementation and YouTube videos of projects that you are interested in. And keep tweaking them to your need. As mentioned earlier, my recommended language will be Keras with Tensorflow backend.

Some Optional Resources:

  • Follow 2 minutes Papers in YouTube to get updated with the wonders that researchers are doing with Deep Learning around the world.
  • Twitter can be a fantastic tool to stay updated with new ML inventions.
  • If you get stuck, there are many groups and communities in Reddit and facebook where people will help you out. Don’t hesitate to ask for help.

Conclusion:

Machine Learning and Deep Learning is one of the most fascinating technology in the world right now. And unlike many other sectors, there are less qualified Deep Learning experts than the industry needs. So in career prospect too Deep Learning is an appealing field.

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Masum Hasan

Masum Hasan

Researcher in NLP and Machine Learning | masumhasan.net