Details for this torrent 

Python Machine Learning By Example- Yuxi Liu(ePUB)
Type:
Other > E-books
Files:
1
Size:
14.33 MiB (15025569 Bytes)
Texted language(s):
English
Tag(s):
nonfiction programming reference computers
Uploaded:
2019-10-10 19:48:48 GMT
By:
1Edge VIP
Seeders:
24
Leechers:
2
Comments
0  

Info Hash:
FCD59231040C27D1DD2E45F4AF41594F4705BFD6




(Problems with magnets links are fixed by upgrading your torrent client!)
Python Machine Learning By Example

	
By Yuxi Liu

Published by Packt Publishing in 2019 

382 pages

EPUB, 14.33 MB


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
A surging interest in machine learning is due to the fact that it evolutionzies automation by learning patterns in data and using them to make predictions and decisions. Your ML journey starts with this book, as the second edition of the bestseller, Python Machine Learning By Example.

Hayden's unique insights and expertise introduce you to important ML concepts and implementations of algorithms in Python both from scratch and with libraries. Each chapter of the book walks you through an industry adopted application. With the help of realistic examples, you will find it intriguing to acquire mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP - they are no more obscure as you thought.

This critically extended and updated edition now includes implementation with trendy libraries including TensorFlow, gensim and Keras. The scikit-learn codes are also fully modernized. Even if you've read the last edition, you'll still be delighted to find plenty of new content, for example, neural network, dimensionality reduction, topic modeling, large-scale learning with Spark and word embedding