Machine Learning Fundamentals

Name: Machine Learning Fundamentals | Concepts, Models, and Applications
Brand: Business Expert Press
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Concepts, Models, and Applications

Amar Sahay Rajeev Sahay(Autor*in)

Business Expert Press

Erschienen am 18. März 2025

264 Seiten

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Front cover
Half title
Title
Copyright
Description
Contents
Preface
Acknowledgments
Introduction
Primary Audience
Scope
PART 1 Data Science and Its Elements
CHAPTER 1 Data Science and Machine Learning (ML) An Overview and Scope of Data Science
Chapter Highlights
Introduction
What Is Data Science?
Role of Statistics in Data Science
Conflicting Definitions of Data Science and Its Relation to Statistics
A Brief History and Predictions for Data Science
Difference Between Data Science and Data Analytics?
Knowledge and Skills for Data Science Professionals
Broad View of Data Science and Body of Knowledge
Data Science and Machine Learning (ML)
Application Areas of Machine Learning (ML)
Career Path for Data Science Professionals and Data Scientists
Outlook
Summary
CHAPTER 2 Introduction and Basics of Machine Learning (ML)
What Is Machine Learning (ML)?
Why Machine Learning?
Machine Learning Problem-Solving Approach
Supervised Learning
Unsupervised Learning
Difference Between Supervised and Unsupervised Learning
Overview of Machine Learning Models
Regression
Simple and Multiple Regression Models
Other Regression Models
Classification
Different Types of Classification Tasks in Machine Learning
Binary Classification
MultiClass Classification
Commonly Used Binary Classification Algorithms
Types of Naïve Bayes Classifier
Support Vector Machines (SVM)
The K-Nearest Neighbor
Random Forest Algorithm
Neural Networks
Deep Learning [8]
Unsupervised Learning
Common Unsupervised Learning Approaches
Clustering
Types of Clustering Methods
Exclusive and Overlapping Clustering
K-Means Clustering
Hierarchical Clustering
Mean Shift Clustering Algorithm
Gaussian Mixture Model
Association
Dimensionality Reduction
SemiSupervised Machine Learning Algorithms
Reinforcement Learning (RL)
Some Fundamental Concepts Related to Machine Learning
HoldOut
Cross Validation
Bootstrapping
Signal and Noise?
Goodness-of-Fit
Bias and Variance
How to Compute Bias and Variance?
Lazy Learners and Eager Learners
Summary
CHAPTER 3 Machine Learning Process, Associated Areas, and Managing Data in ML Models
Chapter Highlights
Need and Justification for the Use of Machine Learning Approach
Machine (ML) Process and Applications
Machine Learning Problem-Solving Steps
Machine Learning and Associated Areas
Subsets of Machine Learning (ML)
Machine Learning and Data Mining
Other Associated Areas of Machine Learning
Optimization
Generalization
Physics
Statistics
A Brief Overview of Statistical Tools for Machine Learning
General Statistics Skills
Machine Learning (ML) Process and the Types of Data in ML Process
Data for ML Models and Managing Data Throughout ML Process
Training Data Set
Validation Data Set[3]
Hyperparameters
Test Data Set
Determining the Size of Data Sets
Machine Learning Problem-Solving Steps
Fine-Tuning the Model
Summary
PART 2 Machine Learning (ML) Libraries, Data Representation, Problem Formulation, and EDA
CHAPTER 4 Libraries and Algorithms Used in Machine Learning, Data, and ML Models
Chapter Highlights
Introduction: Prerequisites
Libraries and Algorithms Used in Machine Learning
Jupyter Notebook
Python
Scikit-Learn
Pandas
Visualization With Matplotlib
NumPy
Seaborn
Some Other Technologies Used in Machine Learning
Summary
CHAPTER 5 Working With Data
Chapter Highlights
Loading a Data Set and creating Feature and Target Matrices
Importing Data Using Python and Seaborn in Jupyter Notebook
Importing Data Using Seaborn Into a Jupyter Notebook and Creating Feature and Target Matrices
Reading CSV File Into a Jupyter Notebook Using Pandas and Creating Feature and Target Matrices.
Preprocessing the Entire Data Set
Outliers and Missing Values in Data Sets (Messy Data)
Outliers
Dealing With Categorical Features
Summary
PART 3 Machine Learning Models
CHAPTER 6 Supervised Learning Models
Chapter Highlights
Supervised Learning Models and Applications
Regression Models
Classification Models
Summary
Introduction
Supervised Learning
Training the System
Simple Supervised Machine Learning Models
Linear Models-Univariate Linear Regression
Interpreting the Fitted Regression Line
The Standard Error of the Estimate (s)
The Coefficient of Determination and Its Meaning
Multivariate Linear Regression-n Explanatory Variables
Multiple Regression Model
Computer Analysis of Multiple Regression
The Regression Equation
Interpreting the Regression Equation
Standard Error of the Estimate (s) and Its Meaning
Error Variance
The Coefficient of Multiple Determination
Multicollinearity in Regression Models
Multicollinearity and Autocorrelation in Multiple Regression
Effects of Multicollinearity
Detecting Multicollinearity
Quadratic (Second Order) Model
Other Types of Regression Algorithms
Lasso and Ridge Regressions
Lasso Regression
Ridge Regression
Linear Predictor Functions
Notations
Other Supervised Learning Models
Statistical Binary Classification
Logistic Regression
Linear Discriminant Analysis (LDA)
Example
Support Vector Machine (SVM)
What Are Support Vectors?
What Is a Hyperplane?
Finding the Right Hyperplane
Applications
Classification and Regression Trees (CART)
Data Considerations for CART Classification
Types of Classification Algorithms
Decision Trees
Gini Impurity
Entropy
Advantages of CART
Limitations of CART
Applications of CART
Difference Between Classification and Regression Tree
Regression Tree
Random Forest Classifier
Naïve Bayes Classifier
Bayes Theorem
Assumptions of Naïve Bayes
K-Nearest Neighbors
K-Nearest Neighbors Algorithm
How Does the K-Nearest Neighbors Algorithm Work?
Another Example of Steps
Factors Influencing the Outcomes:
Choosing the Right Value for K
Another Example
Summary
CHAPTER 7 Unsupervised Learning Models
Chapter Highlights
Unsupervised Learning
Supervised Learning Versus Unsupervised Learning
Why Unsupervised Learning?
Some Drawbacks of Unsupervised Learning
Parametric Unsupervised Learning
Nonparametric Unsupervised Learning
Unsupervised Learning Models
Types of Unsupervised Learning Algorithms
Clustering
Types of Clustering Algorithm
Hierarchical Clustering
Measuring the Distance Between Clusters
K-means Clustering
Elbow Method
Principal Component Analysis (PCA)
Steps Involved in PCA
Step 1: Standardizing the Variables
Step 2: Calculating the Covariance Matrix
Step 3: Compute the Eigenvectors and Eigenvalues of the Covariance Matrix to Identify the Principal Components
Anomaly Detection
Summary
CHAPTER 8 Supervised Learning Models Deep Learning Models
Chapter Highlights
Neural Network Fundamentals
Neural Network Architectures
Multilayer Perceptron (MLP)
Convolutional Neural Networks (CNN)
Convolution
Convolutional Layers
Pooling Layers
Other Common Neural Network Architectures
Key Takeaways and Summary
PART 4 Training Machine Learning Models
CHAPTER 9 Training Machine Learning Models
Chapter Highlights
Choosing a Model
Model Training
Model Evaluation
Key Takeaways and Summary
PART 5 Current State of Machine Learning
CHAPTER 10 Real-World Applications of Machine Learning
Chapter Highlights
Introduction
ML in Health Care
Natural Language Processing
Autonomous Vehicles
Recommender Systems
Closing Remarks
Bibliography
About the Authors
Author Biography: Rajeev Sahay
Author Biography: Amar Sahay
Some Related Books/Journal Papers by the Authors
Index
OTHER TITLES IN THE BIG DATA, BUSINESS ANALYTICS, AND SMART TECHNOLOGY COLLECTION
Concise and Applied Business Books
Back cover

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