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Python machine learning by example : easy-to-follow examples that get you up and running with machine learning / Yuxi (Hayden) Liu.

By: Birmingham : Packt, 2017Description: iv, 240 p. : ill. ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781783553112 (pbk.)
  • 1783553111 (pbk.)
Subject(s):
Contents:
1. Getting Started with Python and Machine Learning -- 2. Exploring the 20 newsgroups data set -- 3. Spam email detection with Naïve Bayes -- 4. News topic classification with Support Vector Machine -- 5. Click-through prediction with tree-based algorithms -- 6. Click-through rate prediction with logistic regression -- 7. Stock prices prediction with regression algorithms -- 8. Best practices.
Summary: Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques. Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.What you will learn Exploit the power of Python to handle data extraction, manipulation, and exploration techniques Use Python to visualize data spread across multiple dimensions and extract useful features Dive deep into the world of analytics to predict situations correctly Implement machine learning classification and regression algorithms from scratch in Python Be amazed to see the algorithms in action Evaluate the performance of a machine learning model and optimize it Solve interesting real-world problems using machine learning and Python as the journey unfolds
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Mesa Lab QA76.73 .P98 .L783 2017 1 Checked out 07/01/2024 50583020006775
Total holds: 0

1. Getting Started with Python and Machine Learning -- 2. Exploring the 20 newsgroups data set -- 3. Spam email detection with Naïve Bayes -- 4. News topic classification with Support Vector Machine -- 5. Click-through prediction with tree-based algorithms -- 6. Click-through rate prediction with logistic regression -- 7. Stock prices prediction with regression algorithms -- 8. Best practices.

Data science and machine learning are some of the top buzzwords in the technical world today. A resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning.
This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Moving ahead, you will learn all the important concepts such as, exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation. With the help of various projects included, you will find it intriguing to acquire the mechanics of several important machine learning algorithms - they are no more obscure as they thought. Also, you will be guided step by step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and best practices of applying machine learning techniques.
Through this book, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple language, Python. Interesting and easy-to-follow examples, to name some, news topic classification, spam email detection, online ad click-through prediction, stock prices forecast, will keep you glued till you reach your goal.What you will learn
Exploit the power of Python to handle data extraction, manipulation, and exploration techniques
Use Python to visualize data spread across multiple dimensions and extract useful features
Dive deep into the world of analytics to predict situations correctly
Implement machine learning classification and regression algorithms from scratch in Python
Be amazed to see the algorithms in action
Evaluate the performance of a machine learning model and optimize it
Solve interesting real-world problems using machine learning and Python as the journey unfolds

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