Python Data Science Handbook
This one contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. which is well structured and useful for learning Data Science. Beginners friendly
https://jakevdp.github.io/PythonDataScienceHandbook/

Most Machine Learning articles on Medium are really very bad quality and repetitive. Titles are usually clickbaits. Most start with a story which is utter nonsense and totally not required. In some 5-10% content is useful but most are fully useless. Sorry if I hurt feelings. Credits: Abhishek Thakur, Kaggle Grandmaster

Agree👍 or hearted😢

AI/DL Certificates: No one cares about certificates in our business. Period. If you learn something while you get the certificate, sure. Get one print out and post it on your wall then. It does make an office less boring.

AI/DL Degrees: If you do interesting projects, and you gain understanding and skills out of the projects, then an AI degree is good for you. Otherwise, no one cares about your degrees. What about a PhD or a master? That depends on the quality of your publications, see below.

AI/DL Projects: If you actually do the projects, but not copying from "10-lines to do object detection" you see on-line, and you actually understand the principles of the techniques you are using, then those projects would be great experience for you. Otherwise, no one cares if you had done 1000 projects in your life.

AI/DL Publications: If the writing process makes you think of a novel technique in the field, absolutely! Or you gain fundamental understanding of a class of algorithms when you research the background, sure that matters a lot! That's why graduates who has past publications are usually strong candidates for many big companies ML department. (Make sure you can answer questions about your own publication though!)

AI/DL Blog Posts You Write: If the quality of the blog is like a paper, sure. But if you just copy from "10-lines to do object detection" , no one cares.

Great Programming Skill: If that means you have source-level understanding of a major deep learning framework, wooooh, I would love to see your resume! What if you know 7 different ways to do string manipulation in python? That certainly is an interesting programming skill, but programming skill does not equal to ML skill. So again, no one cares.

In a nutshell, what matters most is whether you genuinely understand the field. Credits: Arthur Chan

Introduction to Deep Learning
MIT's official introductory course on deep learning methods with applications in Computer Vision, NLP, Medicine, robotics, and more!
http://introtodeeplearning.com/

CNN Explainer
Learn Convolutional Neural Network (CNN) in your browser!
https://poloclub.github.io/cnn-explainer/

Made with ML Topics
Your one-stop platform to explore, learn and build all things machine learning.This page is for the best resources of all time by topic.
https://madewithml.com/topics/

Some Notable Recent ML Papers and Future Trends by Aran Komatsuzaki, here is the link

Ben Hammer, Kaggle CTO

PyTorch announced First PyTorch Developer Day starting 8 AM on November 12, 2020 PST. Learn more:
https://pytorch.org/blog/pytorch-developer-day-2020/

Last Friday, Google announced the support of its AutoML algorithm for time series forecasting. According to the announcement, this cloud-based ML forecasting algorithm is fully automated that neither needs feature engineering nor tunning, with high-quality results.
This is come just four days after the Facebook announcement on their deep learning forecasting model - NeuralProphet. It is interesting to see this trend of moving toward ML/DL solutions for time series forecasting. ~Rami Krispin
More details available here: https://ai.googleblog.com/2020/12/using-automl-for-time-series-forecasting.html

Why PyTorch?
https://www.infoworld.com/article/3597904/why-enterprises-are-turning-from-tensorflow-to-pytorch.html

MIT has launched a new FREE course on Machine Learning!
Link to enroll: http://bit.ly/3acnayN

Harvard University has this Free course on Data Science : Machine Learning!

Link to enroll : http://bit.ly/2WtDPFZ

Many Data Science aspirants struggle to find good projects to get a start in Data science or Machine Learning.

Here is the list of few Data Science projects (found on kaggle), it covers Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems)

1. Basic python and statistics

Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset

2. Advanced Statistics

Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset

3. Supervised Learning

a) Regression Problems

How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview

b) Classification problems

Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking

These are the links of competitions, from there previous notebooks can be checked to begin with, Hope it will be helpful 😊😊

"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI
Academia and projects like Kaggle set expectations AI/ML is about model tuning, while data collection, quality and engineering is undervalued as being trivial and boring. The truth is far from that.
https://research.google/pubs/pub49953/