Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.
Offered by
Difficulty Level
Intermediate
Completion Time
12h48m
Language
English
About Book
Who Is This Book For?
This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h48m total length
Introduction to Machine Learning & Scikit-Learn
Making Decisions with Trees
Making decisions with linear equations
Preparing Your Data
Image processing with nearest neighbors
Text Classification - Not all data exists in tables
Neural Networks - Here comes the Deep Learning
Ensembles - When one model is not enough
The Y is as important as the X
Imbalanced Learn - Not even 1% win the lottery
Clustering - Grouping data when no correct answers are provided
Anomaly Detection - Finding Outliers in Data
Recommender System - Learning about users’ taste from their previous interactions
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