Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python

advertisement

Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python

Machine Learning Algorithms with Python

Introduction

    If I ask you about “Machine learning,” you'll probably imagine a robot or something like the Terminator. In reality t, machine learning is involved not only in robotics, but also in many other applications. You can also imagine something like a spam filter as being one of the first applications in machine learning, which helps improve the lives of millions of people. In this chapter, I'll introduce you what machine learning is, and how it works.

What is Machine learning?

    Machine learning (ML) is a discipline of artificial intelligence (AI) that provides machines with the ability to automatically learn from data and past experiences while identifying patterns to make predictions with minimal human intervention. Machine learning methods enable computers to operate autonomously without explicit programming. ML applications are fed with new data, and they can independently learn, grow, develop, and adapt

advertisement

Table of Contents

CHAPTER 1

INTRODUCTION TO MACHINE LEARNING

What is machine learning?

Why machine learning?

When should you use machine learning?

Types of Systems of Machine Learning

Supervised and unsupervised learning

Supervised Learning

The most important supervised algorithms

Unsupervised Learning

The most important unsupervised algorithms

Reinforcement Learning

Batch Learning

Online Learning

Instance based learning

Model-based learning

Bad and Insufficient Quantity of Training Data

Poor-Quality Data

Irrelevant Features

Feature Engineering

CHAPTER 2

CLASSIFICATION

Installation

The MNIST

Measures of Performance

Confusion Matrix

Recall

Recall Tradeoff

ROC

Multi-class Classification

Training a Random Forest Classifier

Error Analysis

Multi-label Classifications

Multi-output Classification

CHAPTER 3

HOW TO TRAIN A MODEL

Linear Regression

Computational Complexity

Gradient Descent

Batch Gradient Descent

Stochastic Gradient Descent

Mini-Batch Gradient Descent

Polynomial Regression

Learning Curves

Regularized Linear Models

Ridge Regression

Lasso Regression

Chapter 4

Different models combinations

Implementing a simple majority classifier

Combining different algorithms for classification with majority vote

Questions

Download full PDF in Comment section

advertisement