Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow

TLDR: In this article, the authors give computers the ability to learn from data training using simple ML Algorithms for Classification ML Classifiers Using scikit-learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying ML to Sentiment Analysis Embedding a ML Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing Multilayer Artificial Neural Networks Parallelizing

Abstract: Table of Contents Giving Computers the Ability to Learn from Data Training Simple ML Algorithms for Classification ML Classifiers Using scikit-learn Building Good Training Datasets - Data Preprocessing Compressing Data via Dimensionality Reduction Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying ML to Sentiment Analysis Embedding a ML Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing Multilayer Artificial Neural Networks Parallelizing Neural Network Training with TensorFlow TensorFlow Mechanics Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data Using Recurrent Neural Networks GANs for Synthesizing New Data RL for Decision Making in Complex Environments