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.

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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

Machine learning consists of constructing models, some of which are based on complicated mathematical concepts, to understand data. Scikit-learn is an open source Python library that is meant to facilitate the process of applying these models to data problems, without much complex math knowledge required.

This chapter first covered an important step in developing a data problem, that is, representing the data in a tabular manner. Then, the steps involved in the creation of features and target matrices, data preprocessing, and choosing an algorithm were also covered.

Finally, after selecting the type of algorithm that best suits the data problem, the construction of the model can begin through the use of the scikit-learn API, which has three interfaces: estimators, predictors, and transformers. Thanks to the uniformity of the API, learning to use the methods for one algorithm is enough to enable their use for others.

With all of this in mind, in the next chapter, we will focus on detailing the process of implementing an unsupervised algorithm to a real-life dataset.

Previous Section
End of Chapter 1
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