Python Machine Learning By Example : Industry adopted applications with the clear demonstration of Machine Learning concepts using Python libraries, 2/e

Yuxi (Hayden) Liu


Grasp machine learning techniques and algorithms with Python, TensorFlow and scikit through real-world examples

Key Features

  • Exploit the power of Python to dive deep into the world of data mining and analytics
  • Learn machine learning algorithms to solve complex challenges faced by data scientists today
  • Use modern Python libraries like Tensorflow and Keras to create smart cognitive actions for your projects

Book Description

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 visualization and preprocessing, feature engineering, classification, regression, clustering, natural language processing, and model performance evaluation, as well as large-scale learning. 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. 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, and popular Python packages and tools such as TensorFlow, scikit-learn, NLTK, and Spark. Interesting and easy-to-follow examples, to name some, news topic modeling and 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

  • Understand the important concepts in machine learning and data science
  • Exploit the power of Python to dive deep into the world of data mining and analytics
  • Scale up model training to million and more data points using Apache Hadoop and Spark
  • Delve deep into text and natural language processing using Python library such NLTK and Gensim
  • Select and build a machine learning model, evaluate its performance and optimize it
  • Master the Implementation of popular classification, regression, clustering and feature engineering algorithms both from scratch in Python and using TensorFlow and scikit-learn

Who This Book Is For

This book is for Machine Learning Aspirants, Data Analysts, Data Engineers who are highly passionate about Machine Learning and wants to start getting employed in Machine Learning assignments. Prior knowledge of python coding is assumed and basic familiarity with the statistical concept is beneficial although not a mandate




- 利用Python的強大功能深入探索數據挖掘和分析領域
- 學習機器學習算法,解決數據科學家面臨的複雜挑戰
- 使用現代Python庫,如TensorFlow和Keras,為項目創建智能認知行為



本書首先介紹機器學習和Python語言,並向您展示如何完成設置。隨著學習的深入,您將學習到所有重要的概念,如探索性數據分析、數據可視化和預處理、特徵工程、分類、回歸、聚類、自然語言處理和模型性能評估,以及大規模學習。通過包含的各種項目,您將發現學習幾個重要機器學習算法的過程非常有趣 - 它們不再像以前那樣晦澀難懂。最後,您將獲得機器學習生態系統的整體概念和應用機器學習技術的最佳實踐。



- 理解機器學習和數據科學的重要概念
- 利用Python的強大功能深入探索數據挖掘和分析領域
- 使用Apache Hadoop和Spark將模型訓練擴展到百萬甚至更多數據點
- 使用Python庫(如NLTK和Gensim)深入研究文本和自然語言處理
- 選擇並構建機器學習模型,評估其性能並進行優化
- 掌握流行的分類、回歸、聚類和特徵工程算法的實現,包括使用Python從頭開始實現以及使用TensorFlow和scikit-learn