Hey guys! This is Manas from csopensource.com – “Your one-stop destination for everything computer science”.


There has been a lot of buzz around Artificial Intelligence and Machine Learning these days. In fact, we here at csopensource have also mentioned these buzzwords a couple of times. For all the people who want to learn ML from scratch, I am making a free ML course here on csopensource.com there are absolutely no prerequisites for this course.

What will we learn?

I believe in learning by example, so I will be explaining the general theory and then giving a fully functional example in Python 3. I will also redirect you to some other pages which explain the topics in a broader view.

  • Classical Machine Learning
    • Classification
    • Regression
    • Decision Trees
    • Support Vector Machines(SVM)
    • K-Nearest Neighbors Classifier (kNN)
    • Clustering using K-Means (Unsupervised Learning)
  • Deep Learning
    • Artificial Neural Network (ANN)
    • Convolutional Neural Network (CNN)
    • Recurrent Neural Networks (RNN) [Basics]
    • Self Organizing Maps (SOM)
    • Restricted Boltzmann Machines (RBM) [Theory]
    • Auto-Encoders (AE) [Theory]
  • Reinforced Learning
    • Q-Learning [Theory]

We have a really rich syllabus to cover. Difficult topics like RBM’s and AE’s cannot be covered fully in this tutorial series. After learning this, you will feel confident about your ML Knowledge. However, this is not the end. ML is a topic where constant changes are made, you should be ready to learn from scarce sources. If you are ready then good for you! Because Day 1 of this course is about to release! Enjoy Deep Learning!


CourseĀ links: