Details

Subject(s)

• Python (Computer program language) [Browse]

Series

Expert insight. [More in this series]

Summary note

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.

Bibliographic references

Includes bibliographical references and index.

Source of description

• Description based on publisher supplied metadata and other sources.
• Description based on print version record.

Contents

• Cover
• Copyright
• Contributors
• Table of Contents
• Preface
• Chapter 1: Getting Started with Machine Learning and Python
• An introduction to machine learning
• Understanding why we need machine learning
• Differentiating between machine learning and automation
• Machine learning applications
• Knowing the prerequisites
• Getting started with three types of machine learning
• A brief history of the development of machine learning algorithms
• Digging into the core of machine learning
• Generalizing with data
• Overfitting, underfitting, and the bias-variance trade-off
• Overfitting
• Underfitting
• The bias-variance trade-off
• Avoiding overfitting with cross-validation
• Avoiding overfitting with regularization
• Avoiding overfitting with feature selection and dimensionality reduction
• Data preprocessing and feature engineering
• Preprocessing and exploration
• Dealing with missing values
• Label encoding
• One-hot encoding
• Dense embedding
• Scaling
• Feature engineering
• Polynomial transformation
• Binning
• Combining models
• Voting and averaging
• Bagging
• Boosting
• Stacking
• Installing software and setting up
• Setting up Python and environments
• Installing the main Python packages
• NumPy
• SciPy
• pandas
• scikit-learn
• TensorFlow
• PyTorch
• Summary
• Exercises
• Chapter 2: Building a Movie Recommendation Engine with Naïve Bayes
• Getting started with classification
• Binary classification
• Multiclass classification
• Multi-label classification
• Exploring Naïve Bayes
• Bayes' theorem by example
• The mechanics of Naïve Bayes
• Implementing Naïve Bayes
• Implementing Naïve Bayes from scratch
• Implementing Naïve Bayes with scikit-learn
• Building a movie recommender with Naïve Bayes
• Preparing the data
• Training a Naïve Bayes model.
• Evaluating classification performance
• Tuning models with cross-validation
• 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
• Constructing a decision tree
• The metrics for measuring a split
• Gini Impurity
• Information gain
• 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
• 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
• Getting started with the logistic function
• Jumping from the logistic function to logistic regression
• Training a logistic regression model
• Training a logistic regression model using gradient descent
• Predicting ad click-through with logistic regression using gradient descent
• Training a logistic regression model using stochastic gradient descent (SGD)
• Training a logistic regression model with regularization
• Feature selection using L1 regularization
• Feature selection using random forest
• Training on large datasets with online learning
• Handling multiclass classification
• Implementing logistic regression using TensorFlow
• Chapter 5: Predicting Stock Prices with Regression Algorithms
• What is regression?
• Mining stock price data
• A brief overview of the stock market and stock prices
• Getting started with feature engineering.
• Acquiring data and generating features
• Estimating with linear regression
• How does linear regression work?
• Implementing linear regression from scratch
• Implementing linear regression with scikit-learn
• Implementing linear regression with TensorFlow
• Estimating with decision tree regression
• Transitioning from classification trees to regression trees
• Implementing decision tree regression
• Implementing a regression forest
• Evaluating regression performance
• Predicting stock prices with the three regression algorithms
• Chapter 6: Predicting Stock Prices with Artificial Neural Networks
• Demystifying neural networks
• Starting with a single-layer neural network
• Layers in neural networks
• Activation functions
• Backpropagation
• Adding more layers to a neural network: DL
• Building neural networks
• Implementing neural networks from scratch
• Implementing neural networks with scikit-learn
• Implementing neural networks with TensorFlow
• Implementing neural networks with PyTorch
• Picking the right activation functions
• Preventing overfitting in neural networks
• Dropout
• Early stopping
• Predicting stock prices with neural networks
• Training a simple neural network
• Fine-tuning the neural network
• Chapter 7: Mining the 20 Newsgroups Dataset with Text Analysis Techniques
• How computers understand language - NLP
• What is NLP?
• The history of NLP
• NLP applications
• Touring popular NLP libraries and picking up NLP basics
• Installing famous NLP libraries
• Corpora
• Tokenization
• PoS tagging
• NER
• Stemming and lemmatization
• Semantics and topic modeling
• Getting the newsgroups data
• Exploring the newsgroups data
• Thinking about features for text data
• Counting the occurrence of each word token
• Text preprocessing.
• Dropping stop words
• Reducing inflectional and derivational forms of words
• Visualizing the newsgroups data with t-SNE
• What is dimensionality reduction?
• t-SNE for dimensionality reduction
• Representing words with dense vectors - word embedding
• Building embedding models using shallow neural networks
• Utilizing pre-trained embedding models
• 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
• How does k-means clustering work?
• Implementing k-means from scratch
• Implementing k-means with scikit-learn
• Choosing the value of k
• Clustering newsgroups dataset
• Clustering newsgroups data using k-means
• Describing the clusters using GPT
• Discovering underlying topics in newsgroups
• Topic modeling using NMF
• Topic modeling using LDA
• Chapter 9: Recognizing Faces with Support Vector Machine
• Finding the separating boundary with SVM
• Scenario 1 - identifying a separating hyperplane
• Scenario 2 - determining the optimal hyperplane
• Scenario 3 - handling outliers
• Implementing SVM
• Scenario 4 - dealing with more than two classes
• One-vs-rest
• One-vs-one
• Multiclass cases in scikit-learn
• Scenario 5 - solving linearly non-separable problems with kernels
• Choosing between linear and RBF kernels
• Classifying face images with SVM
• Exploring the face image dataset
• Building an SVM-based image classifier
• Boosting image classification performance with PCA
• Estimating with support vector regression
• Implementing SVR
• Chapter 10: Machine Learning Best Practices
• Machine learning solution workflow
• Best practices in the data preparation stage.
• Best practice 1 - Completely understanding the project goal
• Best practice 2 - Collecting all fields that are relevant
• Best practice 3 - Maintaining the consistency and normalization of field values
• Best practice 4 - Dealing with missing data
• Best practice 5 - Storing large-scale data
• Best practices in the training set generation stage
• Best practice 6 - Identifying categorical features with numerical values
• Best practice 7 - Deciding whether to encode categorical features
• Best practice 8 - Deciding whether to select features and, if so, how to do so
• Best practice 9 - Deciding whether to reduce dimensionality and, if so, how to do so
• Best practice 10 - Deciding whether to rescale features
• Best practice 11 - Performing feature engineering with domain expertise
• Best practice 12 - Performing feature engineering without domain expertise
• Binarization and discretization
• Interaction
• Best practice 13 - Documenting how each feature is generated
• Best practice 14 - Extracting features from text data
• tf and tf-idf
• Word embedding
• Word2Vec embedding
• Best practices in the model training, evaluation, and selection stage
• Best practice 15 - Choosing the right algorithm(s) to start with
• Naïve Bayes
• Logistic regression
• SVM
• Random forest (or decision tree)
• Neural networks
• Best practice 16 - Reducing overfitting
• Best practice 17 - Diagnosing overfitting and underfitting
• Best practice 18 - Modeling on large-scale datasets
• Best practices in the deployment and monitoring stage
• Best practice 19 - Saving, loading, and reusing models
• Saving and restoring models using pickle
• Saving and restoring models in TensorFlow
• Saving and restoring models in PyTorch
• Best practice 20 - Monitoring model performance
• Best practice 21 - Updating models regularly.
• Summary.

Show 224 more Contents items

ISBN

9781835082225

OCLC

1451040808

Statement on language in description

Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...