Book Image

Machine Learning with Python

By : Oliver Theobald
Book Image

Machine Learning with Python

By: Oliver Theobald

Overview of this book

The course starts by setting the foundation with an introduction to machine learning, Python, and essential libraries, ensuring you grasp the basics before diving deeper. It then progresses through exploratory data analysis, data scrubbing, and pre-model algorithms, equipping you with the skills to understand and prepare your data for modeling. The journey continues with detailed walkthroughs on creating, evaluating, and optimizing machine learning models, covering key algorithms such as linear and logistic regression, support vector machines, k-nearest neighbors, and tree-based methods. Each section is designed to build upon the previous, reinforcing learning and application of concepts. Wrapping up, the course introduces the next steps, including an introduction to Python for newcomers, ensuring a comprehensive understanding of machine learning applications.

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Table of Contents (18 chapters)
Free Chapter
1
FOREWORD
FOREWORD
2
DATASETS USED IN THIS BOOK
DATASETS USED IN THIS BOOK
3
INTRODUCTION
INTRODUCTION
4
DEVELOPMENT ENVIRONMENT
DEVELOPMENT ENVIRONMENT
5
MACHINE LEARNING LIBRARIES
MACHINE LEARNING LIBRARIES
6
EXPLORATORY DATA ANALYSIS
EXPLORATORY DATA ANALYSIS
7
DATA SCRUBBING
DATA SCRUBBING
8
PRE-MODEL ALGORITHMS
PRE-MODEL ALGORITHMS
9
SPLIT VALIDATION
SPLIT VALIDATION
10
MODEL DESIGN
MODEL DESIGN
11
LINEAR REGRESSION
LINEAR REGRESSION
12
LOGISTIC REGRESSION
LOGISTIC REGRESSION
13
SUPPORT VECTOR MACHINES
SUPPORT VECTOR MACHINES
14
k-NEAREST NEIGHBORS
k-NEAREST NEIGHBORS
15
TREE-BASED METHODS
TREE-BASED METHODS
16
NEXT STEPS
NEXT STEPS
APPENDIX 1: INTRODUCTION TO PYTHON
APPENDIX 1: INTRODUCTION TO PYTHON
APPENDIX 2: PRINT COLUMNS
APPENDIX 2: PRINT COLUMNS

DEVELOPMENT ENVIRONMENT

 

As the practical exercises delivered in this book use Jupyter Notebook as the development environment for Python 3, this chapter serves as an optional guide for installing Jupyter Notebook. If you have prior experience using Jupyter Notebook or have read my earlier title Machine Learning for Absolute Beginners, then you may wish to proceed to the next chapter.

 

Jupyter Notebook is a popular choice for practitioners and online courses alike, as it combines live code, explanatory notes, and visualizations into one convenient workspace and runs from any web browser.

Jupyter Notebook can be installed using the Anaconda Distribution or Python’s package manager, pip. As an experienced Python user, you may wish to install Jupyter Notebook via pip, and there are instructions available on the Jupyter Notebook website (http://jupyter.org/install.html) outlining this option. For beginners, I recommend choosing the Anaconda Distribution option...

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