CEVWS-Machine Learning


Aman Bakshi


These are courses available freely online.
  1. ML Crash Course by Google - Interactive and very good to clear basics.
  2. Multivariable Calculus (MIT OCW) - A complete course on calculus to learn the required prerequisites.
  3. Linear Algebra (MIT OCW) - Must take course if you are interested in learning Linear Algebra.
  4. Application Specific Deep Learning Course - A free course by fast.ai that helps to implement neural networks.
  5. Computer Vision using Deep Learning
  6. Natural Language Processing using Deep Learning
  7. Courses on Coursera:
  8. Andrew Ng’s ML Course - Beginner course in machine learning. Covers all basic topics in depth.
  9. Deep Learning Specialization - A series of five courses which comprehensively cover all major topics in deep learning.


  1. Neural Networks and Deep Learning by Michael Nielsen - Covers all the basics of Neural Networks with the required maths.
  2. The Deep Learning Book by Ian Goodfellow - In depth information on neural networks and the entire deep learning field.
  3. Hands On ML - A book on implementing ML and DL using tensorflow.

Youtube Playlists

  1. Videos on Matplotlib, plotting and visualization library in python.
  2. Videos on Pandas, data handling library in python.
  3. Neural Networks using Tensorflow
  4. A great course on Deep Learning by Geoffrey Hinton.
  5. Deep Learning for Self driving cars


  1. Kaggle
  2. Machine Hack
  3. Datahack AnalyticsVidhya


The following forums can be used to ask basic as well as complex doubts. The community for ML is really great as long as you are able to explain your problem properly.
  1. https://datascience.stackexchange.com/
  2. https://stats.stackexchange.com/
  3. https://www.reddit.com/r/learnmachinelearning/


A resources library well maintained by members of Cutting Edge Visionaries

Code References

Machine Learning

Topic Books/Resources Remarks Author/Organisation


Stat110x - eDx

Better Explained
Statistics Course - Harvard University
Better Explained (website)
These are no coding concepts.
Joe Blitzstein
- Professor of the Practice in Statistics,
Harvard University


Machine Learning Machine Learning - Stanford University
Deep Learning Complete Specialization
Intro to tensorflow
Covers the very basics with assessments in octave.
Deep Learning covers deep neural nets along
with their optimisation.
Andrew Ng/Coursera
Andrew Ng/Coursera
Andrew Ng (more preferred)
Youtube[Stanford University Classroom course]
Course Website
The instructor is changed, (I’ll upload the
course PDFs for previous instructor later
Andrew Ng
Stanford University
Youtube[Stanford University Classroom course]
Course Website
These are high quality material.
Follow them according to the website.
Fei Fei Li
Stanford University

Deep Learning

Course Video(Youtube Player)
Course Website
Jeremy Howard is past President of Kaggle.
He is a AI Expert, with an Awesome Course.
Remeber to go through setup page, for
setting up system instructions.
Jeremy Howard
(past President-Kaggle)

Blogs & Websites

Colah's Blog
Andrej Karpathy Blogs
Josh Meyer’s Website
Neural Network Visualisation
Machine Learning Mastery
Towards Data Science
A visual introduction to machine learning
Colah's Blog, Andrej Karpathy and Machine Learning Mastery
blogs are highly recommended

Medium Blogs are always good.(In another section)
TowardsDataScience is one of them
There are a lot of blogs in here. Search for the simpler/beginner ones.


Kaggle Datasets
PyTorch Standard Datasets
Academic Torrents

Additional Learning stuff

Education - Google Ai
Intro to Machine Learning
Both are provided by Google Education.
Google Education more to explore
Tensorflow Free Course - Udacity
UDACITY is a great platform to learn.
PCA Explained visually

Data Visualization through pandas and matplotlib in Python
Principal Component Analysis and Visualisation
is very crucial for ML and Data science.
Medium Blogs
NNs and Backpropagation explained in a simple way
Visual Information Theory
What is Exploratory Data Analysis?
AutoML in leadersboard
Google AutoML

**The above mentioned are the quite exciting! ML using ML.
Extraordinary stuff(including visualisation)
K-NN visualization
Decision Tree Visualisation
Bias & Variance Visualisation
Data Handling
Merge and Join