Python Machine Learning

Book description

Python makes machine learning easy for beginners and experienced developers

With computing power increasing exponentially and costs decreasing at the same time, there is no better time to learn machine learning using Python. Machine learning tasks that once required enormous processing power are now possible on desktop machines. However, machine learning is not for the faint of heart—it requires a good foundation in statistics, as well as programming knowledge. Python Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. 

Readers will get started by following fundamental topics such as an introduction to Machine Learning and Data Science. For each learning algorithm, readers will use a real-life scenario to show how Python is used to solve the problem at hand.

•          Python data science—manipulating data and data visualization

•          Data cleansing

•          Understanding Machine learning algorithms
•          Supervised learning algorithms

•          Unsupervised learning algorithms

•          Deploying machine learning models 

Python Machine Learning is essential reading for students, developers, or anyone with a keen interest in taking their coding skills to the next level. 

Table of contents

  1. Cover
  2. Introduction
  3. CHAPTER 1: Introduction to Machine Learning
    1. What Is Machine Learning?
    2. Getting the Tools
    3. Summary
  4. CHAPTER 2: Extending Python Using NumPy
    1. What Is NumPy?
    2. Creating NumPy Arrays
    3. Array Indexing
    4. Reshaping Arrays
    5. Array Math
    6. Array Assignment
    7. Summary
  5. CHAPTER 3: Manipulating Tabular Data Using Pandas
    1. What Is Pandas?
    2. Pandas Series
    3. Pandas DataFrame
    4. Summary
  6. CHAPTER 4: Data Visualization Using matplotlib
    1. What Is matplotlib?
    2. Plotting Line Charts
    3. Plotting Bar Charts
    4. Plotting Pie Charts
    5. Plotting Scatter Plots
    6. Plotting Using Seaborn
    7. Summary
  7. CHAPTER 5: Getting Started with Scikit‐learn for Machine Learning
    1. Introduction to Scikit‐learn
    2. Getting Datasets
    3. Getting Started with Scikit‐learn
    4. Data Cleansing
    5. Summary
  8. CHAPTER 6: Supervised Learning—Linear Regression
    1. Types of Linear Regression
    2. Linear Regression
    3. Polynomial Regression
    4. Summary
  9. CHAPTER 7: Supervised Learning—Classification Using Logistic Regression
    1. What Is Logistic Regression?
    2. Using the Breast Cancer Wisconsin (Diagnostic) Data Set
    3. Summary
  10. CHAPTER 8: Supervised Learning—Classification Using Support Vector Machines
    1. What Is a Support Vector Machine?
    2. Kernel Trick
    3. Types of Kernels
    4. Using SVM for Real‐Life Problems
    5. Summary
  11. CHAPTER 9: Supervised Learning—Classification Using K‐Nearest Neighbors (KNN)
    1. What Is K‐Nearest Neighbors?
    2. Summary
  12. CHAPTER 10: Unsupervised Learning—Clustering Using K‐Means
    1. What Is Unsupervised Learning?
    2. Using K‐Means to Solve Real‐Life Problems
    3. Summary
  13. CHAPTER 11: Using Azure Machine Learning Studio
    1. What Is Microsoft Azure Machine Learning Studio?
    2. Summary
  14. CHAPTER 12: Deploying Machine Learning Models
    1. Deploying ML
    2. Case Study
    3. Deploying the Model
    4. Creating the Client Application to Use the Model
    5. Summary
  15. Index
  16. End User License Agreement

Product information

  • Title: Python Machine Learning
  • Author(s): Wei-Meng Lee
  • Release date: April 2019
  • Publisher(s): Wiley
  • ISBN: 9781119545637