Deep Reinforcement Learning Lectures series from Bootcamp. August 2017. Video materials and slides are provided. Berkeley CA

"100 Days of Machine Learning" tutorial series with codes. Github repo.
Some content example:
* Data Preprocessing,
* Simple Linear Regression,
* Multiple Linear Regression,
* Logistic Regression,
* K nearest neighbours,
* Math Behind Logistic,
* Regression,
* SVM,
.........
* Digging Deeper| Mathplotlib |Pandas |Numpy,
* Heirarchical Clustering

Thanks for Avik Jain for sharing great tutorial

Mini course in Deep Learning with PyTorch. Jupyter Notebook files and Slides also provided.
Here is some content from repo:
* ML and spiral classification,
* CNN,
* Salsa,
* RNN, Word Language model,
* Generative models,
........
* VAE, regularization
Detailed explanation

Amazing Google Sheets feature. Did you know this?

Translate in Google Sheets

Imrove your skills by Learning and Practicing Python, Machine Learning, Deep Learning (beginner, intermediate, advanced topic). Complete tutorial categorized series from data-flair

Faster in Python
with Line-of-Code Completions. Machine-learning applied to programming in Python.

Deep Learning lecture
The full deck of (600+) slides, by Professor Gilles Louppe. PDF file available here:

Deep Learning lecture

"One Model to Rule Them All" Christoph Molnar.
Some experienced toughts how to work effectively with your Machine Learning model(project)

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.

SafeML ICLR 2019 Workshop accepted papers list. Read and explore new horizons of Machine Learning