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Python Machine Learning By Example - Fourth Edition
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Python Machine Learning By Example - Fourth Edition

by Yuxi (Hayden) Liu

Data Science

Book Details

Book Title

Python Machine Learning By Example - Fourth Edition

Author

Yuxi (Hayden) Liu

Publisher

Packt Publishing

Publication Date

2024

ISBN

9781835085622

Number of Pages

519

Language

English

Format

PDF

File Size

6.5MB

Subject

Data science

Table of Contents

  • Cover
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Getting Started with Machine Learning and Python
  • An introduction to machine learning
  • Knowing the prerequisites
  • Getting started with three types of machine learning
  • Digging into the core of machine learning
  • Data preprocessing and feature engineering
  • Combining models
  • Installing software and setting up
  • Summary
  • Exercises
  • Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes
  • Getting started with classification
  • Exploring Naïve Bayes
  • Implementing Naïve Bayes
  • Building a movie recommender with Naïve Bayes
  • Evaluating classification performance
  • Tuning models with cross-validation
  • Summary
  • Exercises
  • References
  • Chapter 3: Predicting Online Ad Click-Through with Tree-Based Algorithms
  • A brief overview of ad click-through prediction
  • Getting started with two types of data – numerical and categorical
  • Exploring a decision tree from the root to the leaves
  • Implementing a decision tree from scratch
  • Implementing a decision tree with scikit-learn
  • Predicting ad click-through with a decision tree
  • Ensembling decision trees – random forests
  • Ensembling decision trees – gradient-boosted trees
  • Summary
  • Exercises
  • Chapter 4: Predicting Online Ad Click-Through with Logistic Regression
  • Converting categorical features to numerical – one-hot encoding and ordinal encoding
  • Classifying data with logistic regression
  • Training a logistic regression model
  • Training on large datasets with online learning
  • Handling multiclass classification
  • Implementing logistic regression using TensorFlow
  • Summary
  • Exercises
  • Chapter 5: Predicting Stock Prices with Regression Algorithms
  • What is regression?
  • Mining stock price data
  • Getting started with feature engineering
  • Estimating with linear regression
  • Estimating with decision tree regression
  • Implementing a regression forest
  • Evaluating regression performance
  • Predicting stock prices with the three regression algorithms
  • Summary
  • Exercises
  • Chapter 6: Predicting Stock Prices with Artificial Neural Networks
  • Demystifying neural networks
  • Building neural networks
  • Picking the right activation functions
  • Preventing overfitting in neural networks
  • Predicting stock prices with neural networks
  • Summary
  • Exercises
  • Chapter 7: Mining the 20 Newsgroups Dataset with Text Analysis Techniques
  • How computers understand language – NLP
  • Touring popular NLP libraries and picking up NLP basics
  • Getting the newsgroups data
  • Exploring the newsgroups data
  • Thinking about features for text data
  • Visualizing the newsgroups data with t-SNE
  • Summary
  • Exercises
  • Chapter 8: Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
  • Learning without guidance – unsupervised learning
  • Getting started with k-means clustering
  • Clustering newsgroups dataset
  • Discovering underlying topics in newsgroups
  • Summary
  • Exercises
  • Chapter 9: Recognizing Faces with Support Vector Machine
  • Finding the separating boundary with SVM
  • Classifying face images with SVM
  • Estimating with support vector regression
  • Summary
  • Exercises
  • Chapter 10: Machine Learning Best Practices
  • Machine learning solution workflow
  • Best practices in the data preparation stage
  • Best practices in the training set generation stage
  • Best practices in the model training, evaluation, and selection stage
  • Best practices in the deployment and monitoring stage
  • Summary
  • Exercises
  • Chapter 11: Categorizing Images of Clothing with Convolutional Neural Networks
  • Getting started with CNN building blocks
  • Architecting a CNN for classification
  • Exploring the clothing image dataset
  • Classifying clothing images with CNNs
  • Boosting the CNN classifier with data augmentation
  • Improving the clothing image classifier with data augmentation
  • Advancing the CNN classifier with transfer learning
  • Summary
  • Exercises
  • Chapter 12: Making Predictions with Sequences Using Recurrent Neural Networks
  • Introducing sequential learning
  • Learning the RNN architecture by example
  • Training an RNN model
  • Overcoming long-term dependencies with LSTM
  • Analyzing movie review sentiment with RNNs
  • Revisiting stock price forecasting with LSTM
  • Writing your own War and Peace with RNNs
  • Summary
  • Exercises
  • Chapter 13: Advancing Language Understanding and Generation with the Transformer Models
  • Understanding self-attention
  • Exploring the Transformer’s architecture
  • Improving sentiment analysis with BERT and Transformers
  • Generating text using GPT
  • Summary
  • Exercises
  • Chapter 14: Building an Image Search Engine Using CLIP: a Multimodal Approach
  • Introducing the CLIP model
  • Getting started with the dataset
  • Finding images with words
  • Summary
  • Exercises
  • Chapter 15: Making Decisions in Complex Environments with Reinforcement Learning
  • Setting up the working environment
  • Introducing OpenAI Gym and Gymnasium
  • Introducing reinforcement learning with examples
  • Solving the FrozenLake environment with dynamic programming
  • Performing Monte Carlo learning
  • Solving the Blackjack problem with the Q-learning algorithm
  • Summary
  • Exercises
  • Packt Page
  • Other Books You May Enjoy
  • Index