Machine Learning

A Comprehensive Beginner's Guide

Akshay B R, Sini Raj Pulari, T S Murugesh, Shriram K Vasudevan

• Publisher: CRC Press
• Year: 2024
• Print ISBN: 9781032676654
• eBook ISBN: 9781040086094
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Data Processing
• Index: Technology & Engineering

Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.

Table of contents

• Cover
• Half-Title Page
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Authors
• Introduction: What is Machine Learning?
• Chapter 1 Exploring the Iris dataset
• Chapter 2 Heart failure prediction with oneAPI
• Chapter 3 Handling water quality dataset
• Chapter 4 Breast cancer classification with hybrid ML models
• Chapter 5 Flower recognition with Kaggle dataset and Gradio interface
• Chapter 6 Drug classification with hyperparameter tuning
• Chapter 7 Evaluating model performance: Metrics for diabetes prediction
• Chapter 8 Parkinson’s disease detection: An overview with feature engineering and outlier analysis
• Chapter 9 Sonar mines vs. rock prediction using ensemble learning
• Chapter 10 Bankruptcy risk prediction
• Chapter 11 Hotel reservation prediction
• Chapter 12 Crop recommendation prediction
• Chapter 13 Brain tumor classification
• Chapter 14 Exploratory data analysis and classification on wine quality dataset with oneAPI
• Chapter 15 Cats vs. Dogs classification using deep learning models optimized with oneAPI
• Chapter 16 Maximizing placement predictions with outlier removal
• Chapter 17 A deep dive into Mushroom classification with oneAPI
• Chapter 18 Smart healthcare: Machine learning approaches for kidney disease prediction with oneAPI
• Chapter 19 A deep dive into multiclass flower classification with ResNet and VGG16 using oneAPI
• Chapter 20 Dive into X (formerly Twitter’s) emotions using oneAPI: Sentiment analysis with NLP
• Index