Book description
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques
Key Features
- Dive into machine learning algorithms to solve the complex challenges faced by data scientists today
- Explore cutting edge content reflecting deep learning and reinforcement learning developments
- Use updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-end
Book Description
Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).
With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.
At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.
Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.
By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
What you will learn
- Understand the important concepts in ML and data science
- Use Python to explore the world of data mining and analytics
- Scale up model training using varied data complexities with Apache Spark
- Delve deep into text analysis and NLP using Python libraries such NLTK and Gensim
- Select and build an ML model and evaluate and optimize its performance
- Implement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learn
Who this book is for
If you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.
Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.
Table of contents
- Preface
- Getting Started with Machine Learning and Python
- Building a Movie Recommendation Engine with Naïve Bayes
- Recognizing Faces with Support Vector Machine
-
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 forest
- Ensembling decision trees – gradient boosted trees
- Summary
- Exercises
-
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 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
- Training a logistic regression model with regularization
- Feature selection using L1 regularization
- Training on large datasets with online learning
- Handling multiclass classification
- Implementing logistic regression using TensorFlow
- Feature selection using random forest
- Summary
- Exercises
- Scaling Up Prediction to Terabyte Click Logs
-
Predicting Stock Prices with Regression Algorithms
- A brief overview of the stock market and stock prices
- What is regression?
- Mining stock price data
- Estimating with linear regression
- Estimating with decision tree regression
- Estimating with support vector regression
- Evaluating regression performance
- Predicting stock prices with the three regression algorithms
- Summary
- Exercises
- Predicting Stock Prices with Artificial Neural Networks
- Mining the 20 Newsgroups Dataset with Text Analysis Techniques
- Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
-
Machine Learning Best Practices
- Machine learning solution workflow
- Best practices in the data preparation stage
-
Best practices in the training sets 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
- Discretization
- Interaction
- Polynomial transformation
- Best practice 13 – Documenting how each feature is generated
- Best practice 14 – Extracting features from text data
- Tf and tf-idf
- Word embedding
- Word embedding with pre-trained models
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
- Exercises
- Categorizing Images of Clothing with Convolutional Neural Networks
-
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 Long Short-Term Memory
- Analyzing movie review sentiment with RNNs
- Writing your own War and Peace with RNNs
- Advancing language understanding with the Transformer model
- Summary
- Exercises
- Making Decisions in Complex Environments with Reinforcement Learning
- Other Books You May Enjoy
- Index
Product information
- Title: Python Machine Learning By Example - Third Edition
- Author(s):
- Release date: October 2020
- Publisher(s): Packt Publishing
- ISBN: 9781800209718
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