Python Machine Learning By Example:Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition


Python Machine Learning By Example:Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn, 3rd Edition
by:Yuxi (Hayden) Liu
Release Finelybook 出版日期:2020
Publisher Finelybook 出版社:Packt Publishing
pages 页数:498
ISBN-13 书号:9781800209718
ISBN-10 书号:1800209711

Book Description
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniques
Python Machine Learning By Example serves as a comprehensive gateway into the world of machine learning (ML).
With six new chapters, including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks (CNNs), predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.
At the same time, the book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.
Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.
By the end of this machine learning Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed on the best practices of applying ML techniques to solve problems.
What you will learn

Understand the important concepts in ML 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 analysis 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 2, PyTorch and scikit-learn

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