Python Machine Learning
This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you’ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Offered by
Difficulty Level
Intermediate
Completion Time
20h44m
Language
English
About Book
Who Is This Book For?
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.
Python Machine Learning
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 20h44m total length
Giving Computers the Ability to Learn from Data
Training Machine Learning Algorithms the Ability to Learn from Data
A Tour of Machine Learning Classifiers Using Scikit-Learn
Building Good Training Sets – Data Preprocessing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables
Working with Unlabeled Data – Clustering Analysis
Implementing a Multilayer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with TensorFlow
Going Deeper: The Mechanics of TensorFlow
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data using Recurrent Neural Networks
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