Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
datasets/housing		datasets/housing
02_Chapter2_Housing_Regression.ipynb		02_Chapter2_Housing_Regression.ipynb
03_Chapter3_MNIST_Dataset_Classification.ipynb		03_Chapter3_MNIST_Dataset_Classification.ipynb
04_Chapter4_training_linear_models.ipynb		04_Chapter4_training_linear_models.ipynb
05_Chapter5_Support_Vector_Machines.ipynb		05_Chapter5_Support_Vector_Machines.ipynb
06_Chapter6_Decision_Trees.ipynb		06_Chapter6_Decision_Trees.ipynb
07_Chapter7_Ensemble_Learning_and_Random_Forests.ipynb		07_Chapter7_Ensemble_Learning_and_Random_Forests.ipynb
08_Chapter8_Dimensionality_Reduction.ipynb		08_Chapter8_Dimensionality_Reduction.ipynb
09_Chapter9_Unsupervised_Learning.ipynb		09_Chapter9_Unsupervised_Learning.ipynb
10_Chapter10_Introduction_to_Artificial_Neural_Networks_with_Keras.ipynb		10_Chapter10_Introduction_to_Artificial_Neural_Networks_with_Keras.ipynb
11_Chapter11_Training_Deep_Neural_Networks.ipynb		11_Chapter11_Training_Deep_Neural_Networks.ipynb
12_Chapter12_Custom_Models_and_Training_with_TensorFlow.ipynb		12_Chapter12_Custom_Models_and_Training_with_TensorFlow.ipynb
13_Loading_and_Preprocessing_Data_with_TensorFlow.ipynb		13_Loading_and_Preprocessing_Data_with_TensorFlow.ipynb
14_Chapter14_Deep_Computer_Vision_Using_Convolutional_Neural_Networks.ipynb		14_Chapter14_Deep_Computer_Vision_Using_Convolutional_Neural_Networks.ipynb
15_Chapter15_Processing_Sequences_Using_RNNs_and_CNNs.ipynb		15_Chapter15_Processing_Sequences_Using_RNNs_and_CNNs.ipynb
16_Chapter16_Natural_Language_Processing_with_RNNs_and_Attention.ipynb		16_Chapter16_Natural_Language_Processing_with_RNNs_and_Attention.ipynb
17_Chapter17_Representation_Learning_and_Generative_Learning_Using_Autoencoders_and_GANs.ipynb		17_Chapter17_Representation_Learning_and_Generative_Learning_Using_Autoencoders_and_GANs.ipynb
18_Chapter18_Reinforcement_Learning.ipynb		18_Chapter18_Reinforcement_Learning.ipynb
19_Chapter19_Training_and_Deploying_TensorFlow_Models_at_Scale.ipynb		19_Chapter19_Training_and_Deploying_TensorFlow_Models_at_Scale.ipynb
LICENSE		LICENSE
README.md		README.md

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Hands-on-ML-with-Scikit-Learn-Keras-TF

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

This repo is home to notes & code that accompanies Part 2 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.

I tried to solve all the Exercises and cover all the Chapters in this book. This book helped me to sharpen my Machine Learning knowledge and my coding skills. Thank you sir Aurelien Geron.

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