Executive Programme in Applied Data Science using Machine Learning & Artificial Intelligence

• Different Data types
• Data cleaning

• Linear Algebra, concept of matrices, Eigen values, Eigen vectors and their properties
• Spectral decomposition and Principal component analysis

• Concept of Probability, different distributions: Bernoulli, Binomial, Geometric, Hypergeometric, Negative Binomial
• Normal, Exponential, Chi-square and their properties: mean variance
• Covariance and correlation and some mathematical properties
• Concept of Parametric Estimation and Testing of Hypothesis

• Bivariate regression, properties of the regression coefficients
• Extension to multivariate regression

• Propositional Logic, First Order Predicate Logic, Well-formed formulas

• Difference between Regression and classification
• Data Normalization
• Data transformation
• Naïve Bayes Classifier
• Logistic regression
• Decision trees ID3, C5, SLIQ
• PCA, Feature Selection
• Applications
• Practice with Scikit learn and Keras

• Random forest
• Neural Networks Perceptron model, Multi layered feedforward Neural Network
• Activation functions
• Applications
• Practice with Scikit learn and Keras
• Applications of clustering

• Clustering
• What is clustering?
• Difference between classification and clustering
• Clustering techniques
• Practice with Scikit learn and Keras

• DNN, LSTM, CNN
• Practice with Keras and Tensorflow

• Machine Translation
• Text Summarization
• Concept of Word Vectors

• Linear programming
• Integer programming
• Convex optimization

Recommender System

• Classic and recent approaches for different recommender systems
• Product recommendation, Movie recommendation

• Chat GPT + LLM + Generative AI

• Conversational Systems + Applications of AI/ML and Future Scope + Discussion

Real time applications in the area of:

• Stock Market Prediction
• Credit Card Fraud Detection
• Crime Analytics
• Goods and Services Tax
• Bioinformatics