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

Naïve Bayes Algorithm

Naïve Bayes is a classification algorithm based on Bayes' Theorem that naively assumes independency between features and assigns the same weights (degree of importance) to all features. This means that the algorithm assumes that no single feature correlates to or affects another. For example, although weight and height are somehow correlated when predicting a person's age, the algorithm assumes that each feature is independent. Additionally, the algorithm considers all features equally important. For instance, even though the education degree may influence the earnings of a person to a greater degree than the number of children the person has, the algorithm still considers both features equally important.

Although real-life datasets contain features that are not equally important, nor independent, this algorithm is popular among scientists, as it performs surprisingly well over large datasets. Also, it is worth mentioning that thanks to its simplistic approach, it runs...

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