Dive into Machine Learning
Contents
Tools you'll need
More ways to "Dive into Machine Learning"
Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili 5.1k
updated 2mo ago
microsoft/Data-Science-For-Beginners 34.3k
updated 4d ago
microsoft/ML-For-Beginners 84.7k
updated 4d ago
Microsoft/Data-Science-For-Beginners
ujjwalkarn/Machine-Learning-Tutorials 17.7k
updated 1y ago
Machine Learning for Software Engineers, by Nam Vu 28.7k
updated 1y ago
In their words, it's a "top-down and results-first approach designed for software engineers." Definitely bookmark and use it. It can answer many questions and connect you with great resources.
josephmisiti/awesome-machine-learning 72.1k
updated 10d ago
Explore another notebook
Various topical notebooks
Skilling up
Another way: try doing some practice studies
More machine learning career-related links
Prof. Andrew Ng's _Machine Learning_ on Coursera
Public datasets and pet projects
Other courses
Some communities to know about!
More free online courses I've seen recommended. (Machine Learning, Data Science, and related topics.)
Supplement: Troubleshooting
snoop 1.4k
updated 2mo ago
pandas-log
OpenReview organization on GitHub 32
updated 21d ago
Some further releases are pending a professional security review of the codebase.
EthicalML/awesome-artificial-intelligence-guidelines 1.4k
updated 21d ago
visenger/awesome-ml-model-governance 126
updated 1y ago
visenger/awesome-mlops 13.8k
updated 1y ago
eugeneyan/applied-ml 28.7k
updated 1y ago
Privacy-preserving machine learning
Risks - some starting points
Easier sharing of deep learning models and demos
Deep Learning
More deep learning links
Prof. Andrew Ng's
courses on Deep Learning
fastai/fastbook 24.8k
updated 1y ago
by Jeremy Howard and Sylvain Gugger — "an introduction to deep learning, fastai and PyTorch."
explosion/thinc 2.9k
updated 2d ago
is an interesting library that wraps PyTorch, TensorFlow and MXNet models.
labmlai/annotated_deep_learning_paper_implementations 66.1k
updated 2mo ago
Implementations/tutorials of deep learning papers with side-by-side notes. 50+ of them! Really nicely annotated and explained.