- 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