Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

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Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Sets - Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Embedding a Machine Learning Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with TensorFlow Going Deeper - The Mechanics of TensorFlow Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data using Recurrent Neural Networks

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