Introduction to Machine Learning and Neural Classification
By Trilokesh Khatri
Length8h 39m
About this audiobook
Introduction to Machine Learning and Neural Classification is your gateway to understanding the fundamental aspects of machine learning, a subset of AI, as well as neural networks and statistical classification. As machine learning becomes increasingly integral to our lives, this book covers every significant topic with clarity and precision.
We start with an introduction to key terms like Data Science, Machine Learning, Data Mining, Neural Networks, and Statistical Classification. We then explore classical and modern statistical techniques and methods. The book dives into decision tree rules in machine learning and covers neural networks, including methods of comparison and empirical analysis.
Readers will also learn about descriptive statistics, knowledge representation, control dynamic systems, and data mining algorithms. Throughout the book, explanatory diagrams, bar graphs, and tables are provided to enhance understanding.
Whether you're a beginner or looking to deepen your knowledge, this book provides comprehensive insights into these fascinating topics, making it an essential read for anyone interested in machine learning and AI.
Audiobook details
GenreTechnology, Science and Nature
Length8 hrs 39 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateJan 3, 2025
LanguageEnglish
Table of contents
1Chapter 1. Introduction
21.1 Introduction to Data Science
31.2 Introduction to Machine learning
41.3 Introduction to Data Mining
51.4 Introduction to Neural Network
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61.5 Introduction to Statistical classification
71.6 Summary
81.7 Questions
9Chapter 2. Classifications In Machine Learning
102.1 Classification in Machine Learning
112.2 Classification Terminologies In Machine
12Learning
132.3 Types Of Learners In Classification
142.4 Classification Algorithms
152.5 Artificial Neural Networks
162.6 Support Vector Machine
172.7 Classifier Evaluation
182.8 Summary
192.9 Questions
20Chapter 3. Classical Statistical Methods
213.1 Introduction
223.2 Statistical Issues In Data Mining
233.3 Modeling Relationships using Regression
24 Models
25Hypotheses Testing
263.5 Model (Variables or Features) Selection
27using FDR Penalization in GLM
283.6 Summary
293.7 Questions
30Chapter 4. Modern Statistical Techniques
314.1 Introduction
324.1 Linear regression
334.2 Linear Regression with Information Scores
344.3 Modeling with Genetic Algorithms
354.4 Molecular Spectroscopy Theory
364.5 Regression of Power Series
374.6 Scored Regression in Spectroscopy
384.7 Genetic Algorithms for Hyperellipsoidal
39Clustering
404.8 Genetic Algorithm with Regularized
41Mahalanobis Distance (GARM)
424.9 Summary
434.10 Questions
44Chapter 5. Rules And Trees In Machine Learning
455.1 How can an algorithm be represented as
46a tree?
475.2 Types of Decision Trees
485.3 Advantages of Tree-based Machine Learning
49Methods
505.4 Disadvantages of Tree-based Machine
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