Machine Learning Pocket Reference: Working with Structured Data in Python
Book information
Description
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: • Classification, using the Titanic dataset • Cleaning data and dealing with missing data • Exploratory data analysis • Common preprocessing steps using sample data • Selecting features useful to the model • Model selection • Metrics and classification evaluation • Regression examples using k-nearest neighbor, decision trees, boosting, and more • Metrics for regression evaluation • Clustering • Dimensionality reduction • Scikit-learn pipelines
Similar books
Python Feature Engineering Cookbook: Over 70 Recipes for Creating, Engineering, and Transforming Features to Build Machine Learning Models
2020 · PDF
Thoughtful Machine Learning With Python
Learn TensorFlow 2.0: Implement Machine Learning And Deep Learning Models With Python
2020 · PDF
Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis
2019 · PDF
Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem
2018 · PDF
Deep Learning from Scratch: Building with Python from First Principles
2019 · EPUB
Hands-On Q-Learning with Python: Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
2019 · PDF
Python for Probability, Statistics, and Machine Learning 2nd Ed.
2019 · PDF