If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

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