Hands-on Supervised Learning with Python

Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python (English Edition)

Madeleine Shang

• Publisher: BPB Publications
• Year: 2020
• ISBN:
  9789389328974
• Topic: Ciencia de la computación
• Subtopic: Tratamiento de datos

Hands-On ML problem solving and creating solutions using Python. Key Features

• Introduction to Python Programming
• Python for Machine Learning
• Introduction to Machine Learning
• Introduction to Predictive Modelling, Supervised and Unsupervised Algorithms
• Linear Regression, Logistic Regression and Support Vector Machines
• 
  Description
  You will learn about the fundamentals of Machine Learning and Python programming post, which you will be introduced to predictive modelling and the different methodologies in predictive modelling. You will be introduced to Supervised Learning algorithms and Unsupervised Learning algorithms and the difference between them.
  We will focus on learning supervised machine learning algorithms covering Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees and Artificial Neural Networks. For each of these algorithms, you will work hands-on with open-source datasets and use python programming to program the machine learning algorithms. You will learn about cleaning the data and optimizing the features to get the best results out of your machine learning model. You will learn about the various parameters that determine the accuracy of your model and how you can tune your model based on the reflection of these parameters. What You Will Learn
  
• Get a clear vision of what is Machine Learning and get familiar with the foundation principles of Machine learning.
• Understand the Python language-specific libraries available for Machine learning and be able to work with those libraries.
• Explore the different Supervised Learning based algorithms in Machine Learning and know how to implement them when a real-time use case is presented to you.
• Have hands-on with Data Exploration, Data Cleaning, Data Preprocessing and Model implementation.
• Get to know the basics of Deep Learning and some interesting algorithms in this space.
• Choose the right model based on your problem statement and work with EDA techniques to get good accuracy on your model
• 
  Who this book is for
  This book is for anyone interested in understanding Machine Learning. Beginners, Machine Learning Engineers and Data Scientists who want to get familiar with Supervised Learning algorithms will find this book helpful. Table of Contents
  1. Introduction to Python Programming
  2. Python for Machine Learning
  3. Introduction to Machine Learning
  4. Supervised Learning and Unsupervised Learning
  5. Linear Regression: A Hands-on guide 6. Logistic Regression – An Introduction
  7. A sneak peek into the working of Support Vector machines(SVM)
  8. Decision Trees
  9. Random Forests
  10. Time Series models in Machine Learning
  11. Introduction to Neural Networks
  12. Recurrent Neural Networks
  13. Convolutional Neural Networks
  14. Performance Metrics
  15. Introduction to Design Thinking
  16. Design Thinking Case Study About the Author
  Gnana Lakshmi T C —iis Technology Geek, Innovator, Keynote speaker, Community builder and holds a Bachelor degree in Computer Science from National Institute of Technology, Tiruchirappalli. She is currently associated with WileyNXT as Product Manager; Emerging Technologies. She is also a Fellow Alumni at WomenWhoCode and started WomenWhoCode Blockchain community (www.womenwhocode.com/blockchain). She harnesses her knowledge by sharing it with others by conducting live events like webinars and workshops and through online channels like tutorials, social media posts etc. She has conducted several meetups on Machine learning, Blockchain and various other emerging technology topics including a recent meetup at the International Open UP Summit on GPT-3. LinkedIn Profile: https://www.linkedin.com/in/gyan-lakshmi Madeleine Shang —is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart. LinkedIn Profile: https://www.linkedin.com/in/madeleine-shang/

CHAPTER 1

Introduction to Python Programming

Introduction

Python is one of the fastest-growing programming languages in today’s world. It started as a scripting language and now continues to dominate the world of machine learning and data science. From automation to data analysis, Python is the preferred programming language for all the developers due to its use of ease and due to the abundant libraries that make up this language.

Structure

• The origin story
• Python2 versus Python 3
• Python 3 installation
• Basics of Python programming
• Data types
• Flow control statements
• Functions
• Advanced Python programming
• Classes
• Exception handling
• Python sample programs

Objective

This chapter will introduce the basics of Python programming to the readers. You will learn to install Python and its associated packages on your local system and run basic Python programs on your system. You will learn about the different data types and functionalities that make Python a unique programming language.

The origin story

Python 1.0 was released in 2004 in January. Initially, Python was designed to target the mobile operating system, and one of its main attractions was that it provided exception handling. One of the exciting features of Python 1.0 was its essential support for functional programming. In the beginning times, around the release of Python 1.2, it was widely used to promote computer programming, as it is a high-level language and more comfortable to learn as compared with other computer programming languages.

Python has been an open-source programming language from the very beginning, encouraging contributions from across the globe to diversify the features of the language itself. The two significant enhancements while moving from Version 1.0 to 2.0 in the year 2000 were as follows: provisioning a garbage collector and support for Unicode. Python version 3.0 is a relatively new version that was released in 2008. Currently, both versions 2.x and 3.x are being maintained simultaneously and can be used by the programmers.

INFO: Developed by a Dutch programmer, Guido Van Rossum at the National Institute of Mathematics and Computer Science, the Python language is named after a TV show called “Monty Python’s Flying Circus” and not after the python snake.

Python 2 versus Python 3

Developed in 2000, Python 2.x became a universal language very soon. Due to its simplicity and ease of adoption, Python 2.x garnered a lot of community support and continues to still be prevalent in the community. There are several libraries from Python 3 that have been backported for Python 2 because of its continued usage in several companies. Although Python 3 has been released in the year 2008, many organizations still prefer to use Python 2 versions.

Python 2.7 is one of the most famous python versions that is recommended in the industry, but for all practical purposes, Python 3 will be the future of the Python programming language. If you have already learned Python 2, it will not be that difficult to switch to Python 3, as the changes are minimal and easy to adopt. The core features of the language remain the same.

Python 3.7.3 is the latest version of Python and supports a python2to3 utility that allows you to convert your Python 2.x code to Python 3.x seamlessly. It reads your Python 2.x code and applies a series of fixers to transform it into a valid 3.x code. We will be using Python 3 throughout this book, and mentions of Python everywhere will refer to Python 3.

TIP: If you are new to Python programming and are learning Python from scratch, it is highly recommended that you learn Python 3. Python 3.x offers several exciting and improvised features like “standard printing format,” “string is Unicode by default,” “division of integers returning float answers,” “addition of as keyword to import statements,” and many more.

Python 3 installation

Python 3, for all the operating systems, can be downloaded and installed from the official python.org website: https://www.python.org/downloads/release/python-373/.

To check if you already have python installed on your system, you can use the following command on a terminal in a Linux/Mac operating system and a CMD window on a Windows operating system:

python --version

The preceding command will show you the version of Python installed on your system if it exists.

If your system already shows a Python installed (most systems c...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Dedication Page
• About the Authors
• About the Reviewer
• Acknowledgement
• Preface
• Errata
• Table of Contents
• 1. Introduction to Python Programming
• 2. Python for Machine Learning (ML)
• 3. Introduction to Machine Learning (ML)
• 4. Supervised and Unsupervised Learning in Python
• 13. Convolutional Neural Networks
• 14. Performance Metrics
• 15. Design Thinking for ML
• 16. Case Study for Machine Learning