Anuradha Srinivasaraghavan is an academician in the University of Mumbai. Her prime interests are in the areas of machine learning, soft computing, data mining, and databases. She actively participates in content development of the subjects. She also participates in research avenues in the areas of machine learning and soft computing. She completed her postgraduate in 2008 in Computer Engineering. Since then, she has been working in the areas of machine learning, data mining, soft computing.
Contents
Chapter 1 Introduction to Machine Learning 1.1 What is Machine Learning? 1.2 Where is Machine Learning Used? 1.3 Applications of Machine Learning 1.4 Types of Machine Learning
Chapter 2 Model and Cost Function 2.1 Introduction 2.2 Representation of a Model 2.3 Cost Function Notation for Measuring the Accuracy of a Hypothesis Function 2.4 Measuring Accuracy of a Hypothesis Function 2.5 Minimizing the Cost Function for a Single-Variable Function 2.6 Minimizing the Cost Function for a Two-Variable Function 2.7 Role of Gradient Function in Minimizing a Cost Function
Chapter 3 Basics of Vectors and Matrices 3.1 Introduction 3.2 Notations 3.3 Types of Matrices 3.4 Matrix Operations 3.5 Determinant of a Matrix 3.6 Inverse of a Matrix
Chapter 4 Basics of Python 4.1 Introduction 4.2 Installing Python 4.3 Anaconda 4.4 Running Jupyter Notebook 4.5 Python 3: Basic Syntax 4.6 Python Identifiers 4.7 Basic Operators in Python 4.8 Python Decision-Making 4.9 Python Loops 4.10 Numerical Python (NumPy) 4.11 NumPy Matplotlib 4.12 Introduction to Pandas 4.13 Introduction to Scikit-Learn
Chapter 5 Data Preprocessing 5.1 Overview of Data Preprocessing 5.2 Data Cleaning 5.3 Data Integration 5.4 Data Transformation 5.5 Data Reduction or Dimensionality Reduction Part 2 Supervised Learning Algorithms
Chapter 6 Artificial Neural Networks 6.1 Introduction 6.2 Evolution of Neural Networks 6.3 Biological Neuron 6.4 Basics of Artificial Neural Networks 6.5 Activation Functions 6.6 McCulloch–Pitts Neuron Model
Chapter 7 Linear Regression 7.1 Introduction to Supervised Learning and Regression 7.2 Statistical Relation between Two Variables and Scatter Plots 7.3 Steps to Establish a Linear Regression 7.4 Evaluation of Model Estimators 7.5 Solved Problems on Linear Regression
Chapter 8 Logistic Regression 8.1 Introduction to Logistic Regression 8.2 Scenarios Which Require Logistic Regression 8.3 Odds 8.4 Building Logistic Regression Model (Logit Function) 8.5 Maximum Likelihood Estimation 8.6 Example of Logistic Regression
Chapter 9 Decision Tree 9.1 Introduction to Classification and Decision Tree 9.2 Problem Solving Using Decision Trees 9.3 Basic Decision Tree Learning Algorithm 9.4 Popularity of Decision Tree Classifiers 9.5 Steps to Construct a Decision Tree 9.6 Classification Using Decision Trees 9.7 Issues in Decision Trees 9.8 Rule-Based Classification 9.9 Pruning the Rule Set
Chapter 10 Support Vector Machines 10.1 Introduction to Support Vector Machines 10.2 Linear Support Vector Machines 10.3 Optimal Hyperplane 10.4 Basics of Vectors 10.5 Radial Basis Functions
Chapter 11 Bayesian Classification 11.1 Introduction to Bayesian Classifiers 11.2 Naive Bayes Classifier 11.3 Bayesian Belief Networks 11.4 k-Nearest Neighbor (KNN) 11.5 Measuring Classifier Accuracy
Chapter 12 Hidden Markov Model 12.1 Introduction to Hidden Markov Model 12.2 Issues in Hidden Markov Model Part 3 Unsupervised Algorithms
Chapter 13 Introduction to Unsupervised Learning Algorithms 13.1 Introduction to Clustering 13.2 Types of Clustering 13.3 Partitioning Methods of Clustering 13.4 Hierarchical Methods Part 4 Optimization Techniques
Chapter 14 Optimization 14.1 Introduction to Optimization 14.2 Classification of Optimization Problems 14.3 Linear vs Nonlinear Programming Problems 14.4 Unconstrained Minimization Problems 14.5 Gradient-Based Methods (Descent Methods) 14.6 Introduction to Derivative-Free Optimization 14.7 Derivative-Based vs Derivative-Free Optimization
Summary, Multiple-Choice Questions, Very Short Answer Questions, Short Answer Questions, Review Questions
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