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Python Machine Learning by Example

Python Machine Learning by Example

By : Yuxi (Hayden) Liu
4 (20)
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Python Machine Learning by Example

Python Machine Learning by Example

4 (20)
By: Yuxi (Hayden) Liu

Overview of this book

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 Naïve 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.
Table of Contents (17 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Getting Started with Machine Learning and Python
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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
Free Chapter
2
Building a Movie Recommendation Engine with Naïve Bayes
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
Exercise
References
3
Recognizing Faces with Support Vector Machine
Recognizing Faces with Support Vector Machine
Finding the separating boundary with SVM
Classifying face images with SVM
Fetal state classification on cardiotocography
Summary
Exercises
4
Predicting Online Ad Click-Through with Tree-Based Algorithms
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
5
Predicting Online Ad Click-Through with Logistic Regression
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
Feature selection using random forest
Summary
Exercises
6
Scaling Up Prediction to Terabyte Click Logs
Scaling Up Prediction to Terabyte Click Logs
Learning the essentials of Apache Spark
Programming in PySpark
Learning on massive click logs with Spark
Feature engineering on categorical variables with Spark
Summary
Exercises
7
Predicting Stock Prices with Regression Algorithms
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
8
Predicting Stock Prices with Artificial Neural Networks
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
Exercise
9
Mining the 20 Newsgroups Dataset with Text Analysis Techniques
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
10
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
Learning without guidance – unsupervised learning
Clustering newsgroups data using k-means
Discovering underlying topics in newsgroups
Summary
Exercises
11
Machine Learning Best Practices
Machine Learning Best Practices
Machine learning solution workflow
Best practices in the data preparation stage
Best practices in the training sets generation stage
Best practices in the model training, evaluation, and selection stage
Best practices in the deployment and monitoring stage
Summary
Exercises
12
Categorizing Images of Clothing with Convolutional Neural Networks
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
Summary
Exercises
13
Making Predictions with Sequences Using Recurrent 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
14
Making Decisions in Complex Environments with Reinforcement Learning
Making Decisions in Complex Environments with Reinforcement Learning
Setting up the working environment
Introducing reinforcement learning with examples
Solving the FrozenLake environment with dynamic programming
Performing Monte Carlo learning
Solving the Taxi problem with the Q-learning algorithm
Summary
Exercises
15
Other Books You May Enjoy
Other Books You May Enjoy
16
Index
Index

Knowing the prerequisites

Machine learning mimicking human intelligence is a subfield of AI—a field of computer science concerned with creating systems. Software engineering is another field in computer science. Generally, we can label Python programming as a type of software engineering. Machine learning is also closely related to linear algebra, probability theory, statistics, and mathematical optimization. We usually build machine learning models based on statistics, probability theory, and linear algebra, and then optimize the models using mathematical optimization.

The majority of you reading this book should have a good, or at least sufficient, command of Python programming. Those who aren't feeling confident about mathematical knowledge might be wondering how much time should be spent learning or brushing up on the aforementioned subjects. Don't panic: we will get machine learning to work for us without going into any mathematical details in this book. It just requires some basic 101 knowledge of probability theory and linear algebra, which helps us to understand the mechanics of machine learning techniques and algorithms. And it gets easier as we will be building models both from scratch and with popular packages in Python, a language we like and are familiar with.

For those who want to learn or brush up on probability theory and linear algebra, feel free to search for basic probability theory and basic linear algebra. There are a lot of resources available online, for example, https://people.ucsc.edu/~abrsvn/intro_prob_1.pdf regarding probability 101, and http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf regarding basic linear algebra.

Those who want to study machine learning systematically can enroll in computer science, AI, and, more recently, data science master's programs. There are also various data science boot camps. However, the selection for boot camps is usually stricter as they're more job-oriented and the program duration is often short, ranging from four to 10 weeks. Another option is the free Massive Open Online Courses (MOOCs), Andrew Ng's popular course on machine learning. Last but not least, industry blogs and websites are great resources for us to keep up with the latest developments.

Machine learning isn't only a skill but also a bit of sport. We can compete in several machine learning competitions, such as Kaggle (www.kaggle.com)—sometimes for decent cash prizes, sometimes for joy, and most of the time to play to our strengths. However, to win these competitions, we may need to utilize certain techniques, which are only useful in the context of competitions and not in the context of trying to solve a business problem. That's right, the no free lunch theorem (https://en.wikipedia.org/wiki/No_free_lunch_theorem) applies here.

Next, we'll take a look at the three types of machine learning.

End of Section 3
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