https://github.com/akramz/hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron
https://github.com/akramz/hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow

artificial-intelligence deep-learning machine-learning neural-networks notebooks scikit-learn

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Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

• Host: GitHub
• URL: https://github.com/akramz/hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow
• Owner: Akramz
• Created: 2019-09-27T10:01:17.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-18T11:17:33.000Z (9 months ago)
• Last Synced: 2024-10-08T18:06:47.749Z (4 months ago)
• Topics: artificial-intelligence, deep-learning, machine-learning, neural-networks, notebooks, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/tree/master/
• Size: 68.6 MB
• Stars: 799
• Watchers: 28
• Forks: 373
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# Hands-on ML with Scikit-Learn, Keras & TF by Aurelien Geron



This repo is home to notes & code that accompanies Part 1 of Aurelien Geron's "Hands-on ML with Scikit-Learn, Keras & TF" book. The book provides a comprehensive overview of data science, machine learning (with `scikit-learn`), and deep learning (with `tensorflow`).

The Book assumes you know close to nothing about machine learning. It uses production-ready Python frameworks such as:

- `Scikit-Learn`

- `Keras`

- `TensorFlow`

The author favors a hands-on approach through a series of working examples and just a little bit of theory. Prerequesites:

- Some Python programming experience

- Familiarity with NumPy, Pandas, and Matplotlib

- A reasonable understanding of college-level math (calculus, probability, Linear Algebra, and statistics)

The first part of the book is mostly based on `Scikit-Learn`, while the 2nd part is using `Keras/TensorFlow`.

## Roadmap

### The Fundamentals of Machine Learning

We provide links for the available notebooks:

- [The Machine Learning Landscape](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/01.ML_Landscape.ipynb)

- [End-to-End Machine Learning Project](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/02.End-to-End-ML-Project.ipynb)

- [Classification](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/03.Classification.ipynb)

- [Training Models](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/04.Training-Models.ipynb)

- [Support Vector Machines](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/05.SVMs.ipynb)

- [Decision Trees](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/06.Decision_Trees.ipynb)

- [Ensemble Learning and Random Forests](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/07.Ensembles_RFs.ipynb)

- [Dimensionality Reduction](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/08.Dim_Reduction.ipynb)

- [Unsupervised Learning Techniques](https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/blob/master/09.Unsupervised_learning.ipynb)

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