- 830+ hours of Preparation.
- 80+ All India Test Series
- 90+ Books for PCMB
- 180+ Daily Practice Papers.
- Doubt Removal & HOTs Sessions
UNIT‐I
Introduction: Machine
learning, terminologies in
machine learning, Perspectives
and issues in
machine learning,
application of Machine
learning,
Types of machine
learning: supervised, unsupervised,
semi‐supervised learning.
Review of probability, Basic Linear
Algebra in Machine Learning Techniques, Dataset and its types, Data
preprocessing, Bias and Variance in Machine learning , Function approximation,
Overfitting
UNIT‐II
Regression Analysis
in Machine Learning: Introduction
to regression and
its terminologies, Types of Regression, Logistic Regression
Simple Linear
regression:
Introduction to Simple
Linear Regression and
its assumption, Simple
Linear Regression Model Building, Ordinary Least
square estimation, Properties
of the least‐squares
estimators and the fitted
regression model, Interval estimation in simple linear regression , Residuals
Multiple Linear
Regression: Multiple
linear regression model
and its assumption,
Interpret Multiple
Linear
Regression Output (R‐Square,
Standard error, F, Significance F, Coefficient P values)
Access the fit of
multiple
Linear regression
model (R squared, Standard error)
Feature Selection
and Dimensionality Reduction: PCA, LDA, ICA
UNIT‐III
Introduction to
Classification and Classification Algorithms: What is Classification? General Approach to
Classification, k‐Nearest Neighbor
Algorithm, Random Forests, Fuzzy Set Approaches
Support Vector
Machine: Introduction, Types of support vector kernel – (Linear kernel,
polynomial kernel, and Gaussian kernel), Hyperplane – (Decision surface),
Properties of SVM, and Issues in SVM.
Decision Trees: Decision tree
learning algorithm, ID‐3algorithm, Inductive bias, Entropy and information
theory, Information gain, Issues in Decision tree learning.
Bayesian Learning ‐ Bayes theorem,
Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian
belief networks, EM algorithm
Ensemble Methods: Bagging, Boosting
and AdaBoost and XBoost,
Classification
Model Evaluation and Selection:
Sensitivity, Specificity, Positive Predictive Value, Negative
Predictive Value, Lift Curves and Gain
Curves, ROC Curves, Misclassification Cost Adjustment to Reflect Real‐World
Concerns, Decision Cost/Benefit Analysis
UNIT – IV
Introduction to
Cluster Analysis and
Clustering Methods: The Clustering
Task and the Requirements for Cluster
Analysis, Overview of Some Basic Clustering Methods:‐ k‐Means Clustering,
k‐Medoids Clustering, Density‐Based Clustering: DBSCAN ‐Density‐Based
Clustering Based on Connected Regions with High Density, Gaussian Mixture Model
algorithm , Balance Iterative Reducing and Clustering using Hierarchies
(BIRCH), Affinity Propagation clustering algorithm, Mean‐Shift clustering
algorithm, ordering Points to Identify the Clustering Structure (OPTICS)
algorithm, Agglomerative Hierarchy clustering algorithm, Divisive Hierarchical,
Measuring Clustering Goodness
UNIT‐I
Introduction: Machine
learning, terminologies in
machine learning, Perspectives
and issues in
machine learning,
application of Machine
learning,
Types of machine
learning: supervised, unsupervised,
semi‐supervised learning.
Review of probability, Basic Linear
Algebra in Machine Learning Techniques, Dataset and its types, Data
preprocessing, Bias and Variance in Machine learning , Function approximation,
Overfitting
UNIT‐II
Regression Analysis
in Machine Learning: Introduction
to regression and
its terminologies, Types of Regression, Logistic Regression
Simple Linear
regression:
Introduction to Simple
Linear Regression and
its assumption, Simple
Linear Regression Model Building, Ordinary Least
square estimation, Properties
of the least‐squares
estimators and the fitted
regression model, Interval estimation in simple linear regression , Residuals
Multiple Linear
Regression: Multiple
linear regression model
and its assumption,
Interpret Multiple
Linear
Regression Output (R‐Square,
Standard error, F, Significance F, Coefficient P values)
Access the fit of
multiple
Linear regression
model (R squared, Standard error)
Feature Selection
and Dimensionality Reduction: PCA, LDA, ICA
UNIT‐III
Introduction to
Classification and Classification Algorithms: What is Classification? General Approach to
Classification, k‐Nearest Neighbor
Algorithm, Random Forests, Fuzzy Set Approaches
Support Vector
Machine: Introduction, Types of support vector kernel – (Linear kernel,
polynomial kernel, and Gaussian kernel), Hyperplane – (Decision surface),
Properties of SVM, and Issues in SVM.
Decision Trees: Decision tree
learning algorithm, ID‐3algorithm, Inductive bias, Entropy and information
theory, Information gain, Issues in Decision tree learning.
Bayesian Learning ‐ Bayes theorem,
Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian
belief networks, EM algorithm
Ensemble Methods: Bagging, Boosting
and AdaBoost and XBoost,
Classification
Model Evaluation and Selection:
Sensitivity, Specificity, Positive Predictive Value, Negative
Predictive Value, Lift Curves and Gain
Curves, ROC Curves, Misclassification Cost Adjustment to Reflect Real‐World
Concerns, Decision Cost/Benefit Analysis
UNIT – IV
Introduction to
Cluster Analysis and
Clustering Methods: The Clustering
Task and the Requirements for Cluster
Analysis, Overview of Some Basic Clustering Methods:‐ k‐Means Clustering,
k‐Medoids Clustering, Density‐Based Clustering: DBSCAN ‐Density‐Based
Clustering Based on Connected Regions with High Density, Gaussian Mixture Model
algorithm , Balance Iterative Reducing and Clustering using Hierarchies
(BIRCH), Affinity Propagation clustering algorithm, Mean‐Shift clustering
algorithm, ordering Points to Identify the Clustering Structure (OPTICS)
algorithm, Agglomerative Hierarchy clustering algorithm, Divisive Hierarchical,
Measuring Clustering Goodness