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This repository contains all our free resources related to machine learning created and maintained by Amritesh Kumar. The best way to navigate through these resources is to follow our tutorial available on our website, but they can still be used independently. This repository contains:

1. ML Tools: A folder containing all the tools you need to get started in machine learning or deep learning. This section is entirely free, and you must know these libraries to get started in ML.

2. ML Tutorial: This folder contains all our free resources related to tutorials (only the practical section, not theory). You can still use them based on your knowledge.

3. ML Projects: This folder has some ML projects that you can use to practice and learn ML. However, most of the projects are in another repository (AI Projects).

1. https://neuraldemy.com/singular-value-decomposition/
2. https://neuraldemy.com/in-depth-linear-regression-concept-and-application/
3. https://neuraldemy.com/principal-components-analysis/
4. https://neuraldemy.com/linear-discriminant-analysis/
5. https://neuraldemy.com/in-depth-naive-bayes-concept-and-application/
6. https://neuraldemy.com/in-depth-logistic-regression/
7. https://neuraldemy.com/support-vector-machines/
8. https://neuraldemy.com/in-depth-decision-trees-concept-and-application/
9. https://neuraldemy.com/stochastic-gradient-descent/
10. https://neuraldemy.com/in-depth-random-forests-ensemble-learning-concept-and-application/
11. https://neuraldemy.com/nearest-neighbors-concept/
12. https://neuraldemy.com/shop/clustering-and-outlier-detection/
13. https://neuraldemy.com/gaussian-mixture-models/
14. https://neuraldemy.com/deep-learning-introduction-to-anns/
15. https://neuraldemy.com/convolutional-neural-networks-cnns/

NLP tutorial is coming soon 🔥

Practice machine learning and build a portfolio: https://github.com/kelixirr/AI-Projects

If you are stuck or want to ask something, feel free to visit our community https://neuraldemy.com/community/ and raise your query in the forum.

Learn The Basics of Tensorflow 2: https://ryancheunggit.gitbooks.io/tfbook/content/ Machine learning papers: https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap and https://github.com/terryum/awesome-deep-learning-papers