Courses

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
Courses on Coursera:
7. Andrew Ng’s ML Course - Beginner course in machine learning. Covers all basic topics in depth.
8. Deep Learning Specialization - A series of five courses which comprehensively cover all major topics in deep learning.

Books

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

Practice

1. Kaggle
2. Machine Hack
3. Datahack AnalyticsVidhya

Forums

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/

Machine Learning

Topic	Books/Resources	Remarks	Author/Organisation
Mathematics
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
Foundational
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 Coursera
*CS229 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
*CS231n (2017)	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
FastAi	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.
Datasets
data.world Kaggle Datasets PyTorch Standard Datasets DATAHUB 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 https://www.h2o.ai Databoard **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