Book description
Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn
Key Features
- Exploit the power of Python to explore the world of data mining and data analytics
- Discover machine learning algorithms to solve complex challenges faced by data scientists today
- Use Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projects
Book Description
The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you're interested in ML, this book will serve as your entry point to ML.
Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You'll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way.
With the help of this extended and updated edition, you'll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more.
By the end of the book, you'll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.
What you will learn
- Understand the important concepts in machine learning 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 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, and scikit-learn
Who this book is for
If you're a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.
Table of contents
- Title Page
- Copyright and Credits
- About Packt
- Dedication
- Foreword
- Contributors
- Preface
- Section 1: Fundamentals of Machine Learning
- Getting Started with Machine Learning and Python
- Section 2: Practical Python Machine Learning By Example
- Exploring the 20 Newsgroups Dataset with Text Analysis Techniques
- Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms
- Detecting Spam Email with Naive Bayes
-
Classifying Newsgroup Topics with Support Vector Machines
- Finding separating boundary with support vector machines
- Classifying newsgroup topics with SVMs
- More example – fetal state classification on cardiotocography
- A further example – breast cancer classification using SVM with TensorFlow
- Summary
- Exercise
-
Predicting Online Ad Click-Through with Tree-Based Algorithms
- Brief overview of advertising click-through prediction
- Getting started with two types of data – numerical and categorical
- Exploring decision tree from root to leaves
- Implementing a decision tree from scratch
- Predicting ad click-through with decision tree
- Ensembling decision trees – random forest
- Summary
- Exercise
-
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
- Scaling Up Prediction to Terabyte Click Logs
-
Stock Price Prediction with Regression Algorithms
- 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
- Estimating with neural networks
- Evaluating regression performance
- Predicting stock price with four regression algorithms
- Summary
- Exercise
- Section 3: Python 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 practice 6 – identifying categorical features with numerical values
- Best practice 7 – deciding on whether or not to encode categorical features
- Best practice 8 – deciding on whether or not to select features, and if so, how to do so
- Best practice 9 – deciding on whether or not to reduce dimensionality, and if so, how to do so
- Best practice 10 – deciding on whether or not to rescale features
- Best practice 11 – performing feature engineering with domain expertise
- Best practice 12 – performing feature engineering without domain expertise
- Best practice 13 – documenting how each feature is generated
- Best practice 14 – extracting features from text data
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
- Exercises
- Other Books You May Enjoy
Product information
- Title: Python Machine Learning By Example - Second Edition
- Author(s):
- Release date: February 2019
- Publisher(s): Packt Publishing
- ISBN: 9781789616729
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