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Python Machine Learning By Example

Python Machine Learning By Example

By : Yuxi (Hayden) Liu
4.9 (9)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (9)
By: Yuxi (Hayden) Liu

Overview of this book

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.
Table of Contents (18 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
Free Chapter
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
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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
Exercises
References
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3
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 forests
Ensembling decision trees – gradient-boosted trees
Summary
Exercises
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4
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
Summary
Exercises
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5
Predicting Stock Prices with Regression Algorithms
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
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6
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
Exercises
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7
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
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8
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
Getting started with k-means clustering
Clustering newsgroups dataset
Discovering underlying topics in newsgroups
Summary
Exercises
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9
Recognizing Faces with Support Vector Machine
Recognizing Faces with Support Vector Machine
Finding the separating boundary with SVM
Classifying face images with SVM
Estimating with support vector regression
Summary
Exercises
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10
Machine Learning Best Practices
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
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11
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
Advancing the CNN classifier with transfer learning
Summary
Exercises
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12
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 LSTM
Analyzing movie review sentiment with RNNs
Revisiting stock price forecasting with LSTM
Writing your own War and Peace with RNNs
Summary
Exercises
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13
Advancing Language Understanding and Generation with the Transformer Models
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
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14
Building an Image Search Engine Using CLIP: a Multimodal Approach
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
References
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15
Making Decisions in Complex Environments with Reinforcement Learning
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
Join our book’s Discord space
16
Other Books You May Enjoy
Other Books You May Enjoy
17
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.

Most 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 deep 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 build 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, the online course Introduction to Probability by Harvard University (https://pll.harvard.edu/course/introduction-probability-edx) regarding probability 101, and the following paper regarding basic linear algebra: http://www.maths.gla.ac.uk/~ajb/dvi-ps/2w-notes.pdf.

Those who want to study machine learning systematically can enroll in computer science, AI, and, more recently, data science and AI 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 4 to 10 weeks. Another option is free Massive Open Online Courses (MOOCs), such as 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 is not only a skill but also a bit of a sport. We can compete in several machine learning competitions, such as Kaggle (www.kaggle.com)—sometimes for decent cash prizes, sometimes for joy, but 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. In the context of machine learning, this theorem suggests that no single algorithm is universally superior across all possible datasets and problem domains.

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

End of Section 3
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