فهرست مطالب

• 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