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

MACHINE LEARNING LIBRARIES

 

Data scientists rarely work alone. This means it’s vital to maintain consistent code that can be read and reused by other programmers. Similar to using WordPress plugins with websites, code libraries make it easy for data scientists to perform common tasks using pre-written modules of code.

With WordPress, for example, you can install a comments management plugin called Discuz on a portfolio of websites. Using the same plugin for each website eliminates the need for developers to familiarize themselves with each site’s underlying code. They simply need to familiarize themselves with the basic interface and customization settings of the Discuz plugin.

The same logic and benefits apply to machine learning libraries, as complex algorithms and other functions can be called through the same code interface. Moreover, rather than writing the statistical requirements of a regression algorithm over many lines of code, you can call the algorithm...

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