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Python Machine Learning By Example
Python Machine Learning By Example

Python Machine Learning By Example: Implement machine learning algorithms and techniques to build intelligent systems , Second Edition

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Python Machine Learning By Example

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Key benefits

  • 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

Description

The surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions. If you’re interested in ML, this book will serve as your entry point to ML. Python Machine Learning By Example begins with an introduction to important ML concepts and implementations using Python libraries. Each chapter of the book walks you through an industry adopted application. You’ll implement ML techniques in areas such as exploratory data analysis, feature engineering, and natural language processing (NLP) in a clear and easy-to-follow way. With the help of this extended and updated edition, you’ll understand how to tackle data-driven problems and implement your solutions with the powerful yet simple Python language and popular Python packages and tools such as TensorFlow, scikit-learn, gensim, and Keras. To aid your understanding of popular ML algorithms, the book covers interesting and easy-to-follow examples such as news topic modeling and classification, spam email detection, stock price forecasting, and more. By the end of the book, you’ll have put together a broad picture of the ML ecosystem and will be well-versed with the best practices of applying ML techniques to make the most out of new opportunities.

Who is this book for?

If you’re a machine learning aspirant, data analyst, or data engineer highly passionate about machine learning and want to begin working on ML assignments, this book is for you. Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial although not necessary.

What you will learn

  • Understand the important concepts in machine learning 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 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, and scikit-learn
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 28, 2019
Length: 382 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789616729
Vendor :
Google
Category :
Languages :
Tools :

What do you get with Print?

Product feature icon Instant access to your digital eBook copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning
Estimated delivery fee Deliver to Egypt

Standard delivery 10 - 13 business days

$12.95

Premium delivery 3 - 6 business days

$34.95
(Includes tracking information)

Product Details

Publication date : Feb 28, 2019
Length: 382 pages
Edition : 2nd
Language : English
ISBN-13 : 9781789616729
Vendor :
Google
Category :
Languages :
Tools :

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Frequently bought together


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Total $ 126.97
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Total $ 126.97 Stars icon

Table of Contents

14 Chapters
Section 1: Fundamentals of Machine Learning Chevron down icon Chevron up icon
Getting Started with Machine Learning and Python Chevron down icon Chevron up icon
Section 2: Practical Python Machine Learning By Example Chevron down icon Chevron up icon
Exploring the 20 Newsgroups Dataset with Text Analysis Techniques Chevron down icon Chevron up icon
Mining the 20 Newsgroups Dataset with Clustering and Topic Modeling Algorithms Chevron down icon Chevron up icon
Detecting Spam Email with Naive Bayes Chevron down icon Chevron up icon
Classifying Newsgroup Topics with Support Vector Machines Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Tree-Based Algorithms Chevron down icon Chevron up icon
Predicting Online Ad Click-Through with Logistic Regression Chevron down icon Chevron up icon
Scaling Up Prediction to Terabyte Click Logs Chevron down icon Chevron up icon
Stock Price Prediction with Regression Algorithms Chevron down icon Chevron up icon
Section 3: Python Machine Learning Best Practices Chevron down icon Chevron up icon
Machine Learning Best Practices Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
crystalattice Nov 22, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Python ML By Example (BE) is a good complement to Python ML Third Edition (3E). The 3E book focuses on the theory and general application of ML programming, while the BE book focuses an specific application examples.While they both tackle ML programming, their approach is different. The BE book assumes you have a reasonable, foundational background in ML and uses that basis to create specific ML-based applications.For example, whereas 3E has a simple note about Naïve Bayes classification, the BE book has a whole chapter dedicated to the algorithm, discussing the different types of classification methods, how Naïve Bayes works, and then actually implementing a Naïve Bayes application. On the flip side, the 3E book has a whole chapter dedicated just to the different classifiers and different implementations of them using scikit-learn.It's almost like the 3E book is a textbook and the BE book is its complementary workbook for practice. While you may be able to be successful with either one, combining them really maximizes your ML learning.To speak about the BE book in more detail, the topics covered include:*Introduction to Python ML, including software installation*Using Naïve Bayes algorithm to create movie recommendation application*Using SVM for facial recognition*Using tree-based algorithms to predict ad click-through*Using Apache Spark to work with large data sets*Using regression algorithms and neural networks to predict the stock market*Using text analysis and NLP to data mine newsgroups*Using unsupervised learning models to identify newsgroups topics*Using different types of neural networks for different types of analysis approaches*Using reinforcement learning for decision making*ML best practicesIt is a long book (nearly 500 pages), but the material is invaluable for anyone in the ML field, especially if you don't have a lot of experience with the different algorithms. And in conjunction with 3E, you almost have a complete ML curriculum.
Amazon Verified review Amazon
Sunil Thapa Mar 03, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book to review some machine learning algorithms l.
Amazon Verified review Amazon
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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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