Rules of Machine Learning:
Best Practices for ML Engineering
Author: Martin Zinkevich

This document is intended to help those with a basic knowledge of machine learning get thebenefit of best practices in machine learning from around Google.

👉 43 ML Rules to follow
🔗 http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

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Graph ML and deep learning courses

This is another post on your request. Other courses you requested will be shared in following days.

Geometric Deep learning course
AMMI21
👨‍🏫 Teachers: Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković
📚12 lectures, 2 tutorials, and 4 seminars
This course follows GDL BOOK
🔗 Course link: https://geometricdeeplearning.com/lectures/

Machine Learning for Graphs and Sequential Data (MLGS)
by Stephan Günnemann
Awesome course covering in depth generative models, robustness, sequential data, clustering, label propagation, GNNs, and more
🔗 Course link: https://www.in.tum.de/daml/teaching/mlgs/

Stanford CS224W course on graph ML
A legendary Stanford CS224W course on graph ML now releases videos on YouTube for 2021
🎬 60 Videos
⏰ 30h
🔗 Course link


Python For Data Science (Udemy)
This course specifically created for Data Science / AI / ML / DL. It covers BASICS PYTHON ONLY
Rating ⭐️: 4.1 out of 5
Students 👨‍🎓: 65,523 students
Duration ⏰: 3hr 55min of on-demand video
🔗 Course link

Deep Learning Prerequisites: The Numpy Stack in Python V2 (Udemy)
Rating ⭐️: 4.6 out of 5
Students 👨‍🎓: 34,785
Duration ⏰: 1hr 59min of on-demand video
🔗 Course link

There is also this cool blogpost by Gordić Aleksa:
How to get started with Graph Machine Learning

And one early access version book:
Graph Powered Machine Learning
by: Allesandro Negro
🔗 Book link

#graphML #ML #machinelearning #deeplearning #python
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Deep Learning Do It Yourself!

This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left.

https://dataflowr.github.io/website/

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The Periodic Table Of Data Science

🔗 Book link


#machinelearning #ml #datascience
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Lectures for UC Berkeley CS 182: Deep Learning
Spring 2021

🎬 66 videos
⏰ 26 hours

https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A

#deeplearning
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The Incredible PyTorch
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.

https://www.ritchieng.com/the-incredible-pytorch/

#pytorch
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FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning

🎬 30 video lessons with slides
⏰ 28 hours

https://bloomberg.github.io/foml/#home

#machinelearning #ml
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Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.

1 How Models Work
The first step if you're new to machine learning.

2 Basic Data Exploration
Load and understand your data.

3 Your First Machine Learning Model
Building your first model. Hurray!

4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.

5 Underfitting and Overfitting
Fine-tune your model for better performance.

6 Random Forests
Using a more sophisticated machine learning algorithm.

7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.

🔗 Course link

#machinelearning #ml
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The R Programming For Data Science A-Z Complete Diploma 2022
Rating ⭐️: 4.5 out of 5
Students 👨‍🎓: 38,584
Duration ⏰: 5h 6min
🔗 Course link

Free for first 1000 enrollments

Knowledge Graphs Course
Data Models, Knowledge Acquisition, Inference and Applications
Department of Computer Science, Stanford University, Spring 2021

⏳10 weeks, each week has slides and video lessons 📽

https://web.stanford.edu/class/cs520/

#datascience #machinelearning #tensorflow #scikitlearn #keras

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