Hands On Machine Learning with Scikit Learn and TensorFlow Concepts Tools and Techniques to Build Intelligent Systems 1st Edition by Aurelien Geron ISBN 1491962291 9781491962299

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ISBN 10: 1491962291 
ISBN 13: 9781491962299
Author: Aurélien Géron

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworks―scikit-learn and TensorFlow―author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use scikit-learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Hands On Machine Learning with Scikit-Learn and TensorFlow Concepts Tools and Techniques to Build Intelligent Systems 1st Table of contents:

Part I: The Fundamentals of Machine Learning

Chapter 1: Introduction to Machine Learning

1. What Is Machine Learning?
2. The Three Types of Machine Learning
   • Supervised Learning
   • Unsupervised Learning
   • Reinforcement Learning
3. A Brief History of Machine Learning
4. The Machine Learning Workflow
5. Challenges in Machine Learning
6. Introducing Scikit-Learn and TensorFlow

Chapter 2: End-to-End Machine Learning Project

1. Problem Definition and Data Collection
2. Data Preprocessing and Feature Engineering
3. Model Selection and Training
4. Model Evaluation and Tuning
5. Deploying a Model
6. Example: Classifying Housing Prices (with Scikit-Learn)

Part II: Classical Machine Learning Algorithms

Chapter 3: Classification

1. What Is Classification?
2. Logistic Regression
3. k-Nearest Neighbors (k-NN)
4. Support Vector Machines (SVM)
5. Decision Trees and Random Forests
6. Naive Bayes Classifier
7. Performance Metrics for Classification
8. Example: MNIST Handwritten Digit Classification

Chapter 4: Training Models

1. Underfitting and Overfitting
2. Bias-Variance Tradeoff
3. Regularization Techniques
4. Cross-Validation
5. Grid Search and Randomized Search
6. Learning Curves and Model Tuning
7. Example: Optimizing a Classifier Model

Chapter 5: Model Evaluation and Validation

1. Holdout Validation
2. Cross-Validation and k-Fold Cross-Validation
3. Precision, Recall, and F1 Score
4. ROC Curves and AUC Score
5. Multi-Class Classification
6. Confusion Matrix and Performance Metrics
7. Example: Evaluating the Best Model

Chapter 6: Decision Trees and Random Forests

1. Decision Trees: Concept and Construction
2. Random Forests and Ensemble Learning
3. Feature Importance and Model Interpretability
4. Overfitting in Decision Trees
5. Hyperparameter Tuning for Decision Trees
6. Example: Decision Trees for Regression

Part III: Neural Networks and Deep Learning

Chapter 7: Introduction to Neural Networks

1. Perceptrons and Artificial Neurons
2. The Structure of a Neural Network
3. Activation Functions and Backpropagation
4. Training Neural Networks
5. Example: A Simple Neural Network for MNIST

Chapter 8: TensorFlow and Keras

1. Introduction to TensorFlow
2. Installing and Setting Up TensorFlow
3. TensorFlow Basics: Tensors and Operations
4. Keras API: High-Level Neural Network API
5. Building Neural Networks Using Keras
6. Example: Building a Simple Feedforward Neural Network

Chapter 9: Training Deep Neural Networks

1. Problems with Training Deep Networks
2. Vanishing and Exploding Gradients
3. Solutions: Weight Initialization, Batch Normalization, and Dropout
4. Optimization Algorithms: Stochastic Gradient Descent, Adam, RMSprop
5. Hyperparameter Tuning for Deep Networks
6. Example: Training a Deep Neural Network on CIFAR-10

Chapter 10: Convolutional Neural Networks (CNNs)

1. What Is a Convolutional Neural Network?
2. Convolution and Pooling Operations
3. Building a CNN for Image Classification
4. CNN Architectures and Best Practices
5. Transfer Learning and Pretrained Models
6. Example: Image Classification with CNNs

Chapter 11: Recurrent Neural Networks (RNNs)

1. What Are Recurrent Neural Networks?
2. Understanding Time-Series Data and Sequential Models
3. Vanishing and Exploding Gradients in RNNs
4. Long Short-Term Memory (LSTM) Networks
5. Gated Recurrent Units (GRUs)
6. Building an RNN for Text Generation
7. Example: Sentiment Analysis with RNNs

Part IV: Unsupervised Learning

Chapter 12: Clustering

1. Introduction to Clustering
2. k-Means Clustering
3. DBSCAN and Hierarchical Clustering
4. Evaluating Clustering Performance
5. Dimensionality Reduction Techniques
6. Example: Clustering Customers for Segmentation

Chapter 13: Anomaly Detection

1. Introduction to Anomaly Detection
2. Unsupervised Algorithms for Anomaly Detection
3. Isolation Forest and One-Class SVM
4. Example: Detecting Fraud in Credit Card Transactions

Chapter 14: Dimensionality Reduction

1. Introduction to Dimensionality Reduction
2. Principal Component Analysis (PCA)
3. t-Distributed Stochastic Neighbor Embedding (t-SNE)
4. Manifold Learning and Autoencoders
5. Example: Visualizing High-Dimensional Data

Part V: Model Deployment and Production Systems

Chapter 15: Scaling Up with TensorFlow

1. TensorFlow Serving for Model Deployment
2. TensorFlow Lite for Mobile Devices
3. TensorFlow.js for In-Browser Models
4. TensorFlow Extended (TFX) for End-to-End Pipelines
5. Scaling to Distributed Systems with TensorFlow
6. Example: Deploying a TensorFlow Model to Production

Chapter 16: Real-World Machine Learning

1. Building Real-World Applications
2. Data Preprocessing and Cleaning Techniques
3. Handling Imbalanced Datasets
4. Real-World Case Studies and Challenges
5. Future Trends in Machine Learning

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