Book Image

Machine Learning Fundamentals

By : Hyatt Saleh
Book Image

Machine Learning Fundamentals

By: Hyatt Saleh

Overview of this book

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.

Related Content you might be interested in

Current Title:

Small book image
Machine Learning Fundamentals
Hyatt Saleh
Nov, 2018 | 240 pages
Small book image
Applied Deep Learning with PyTorch
Hyatt Saleh
Apr, 2019 | 254 pages
Small book image
The Deep Learning with PyTorch Workshop
Hyatt Saleh
Jul, 2020 | 330 pages
Small book image
Machine Learning with scikit-learn Quick Start Guide
Kevin Jolly
Oct, 2018 | 172 pages
Table of Contents (9 chapters)
Machine Learning Fundamentals
Machine Learning Fundamentals
Preface
Preface
Free Chapter
1
Introduction to Scikit-Learn
Introduction to Scikit-Learn
Introduction
Scikit-Learn
Data Representation
Data Preprocessing
Scikit-Learn API
Supervised and Unsupervised Learning
Summary
2
Unsupervised Learning: Real-Life Applications
Unsupervised Learning: Real-Life Applications
Introduction
Clustering
Exploring a Dataset: Wholesale Customers Dataset
Data Visualization
k-means Algorithm
Mean-Shift Algorithm
DBSCAN Algorithm
Evaluating the Performance of Clusters
Summary
3
Supervised Learning: Key Steps
Supervised Learning: Key Steps
Introduction
Model Validation and Testing
Evaluation Metrics
Error Analysis
Summary
4
Supervised Learning Algorithms: Predict Annual Income
Supervised Learning Algorithms: Predict Annual Income
Introduction
Exploring the Dataset
Naïve Bayes Algorithm
Decision Tree Algorithm
Support Vector Machine Algorithm
Error Analysis
Summary
5
Artificial Neural Networks: Predict Annual Income
Artificial Neural Networks: Predict Annual Income
Introduction
Artificial Neural Networks
Applying an Artificial Neural Network
Performance Analysis
Summary
6
Building Your Own Program
Building Your Own Program
Introduction
Program Definition
Saving and Loading a Trained Model
Interacting with a Trained Model
Summary
Appendix
Appendix
Chapter 1: Introduction to scikit-learn
Chapter 2: Unsupervised Learning: Real-life Applications
Chapter 3: Supervised Learning: Key Steps
Chapter 4: Supervised Learning Algorithms: Predict Annual Income
Chapter 5: Artificial Neural Networks: Predict Annual Income
Chapter 6: Building Your Own Program

Summary

This chapter wraps up all of the concepts and techniques that are required to successfully train a machine learning model based on training data. In this chapter, we introduced the idea of building a comprehensive machine learning program that not only accounts for the stages involved in the preparation of the dataset and creation of the ideal model, but also the stage related to making the model accessible for future use, which is accomplished by carrying out three main processes: saving the model, loading the model, and creating a channel that allows users to easily interact with the model and obtain an outcome. The pickle module was also introduced in this regard.

Further, to make the model accessible to users, the ideal channel (for example, an API, an application, a website or a form) needs to be selected according to the type of user that will interact with the model. Then, an intermediary needs to be programmed, which can connect the channel with the model. This intermediary...

Previous Section
End of Chapter 6
Next Chapter