Module 1 : PYTHON PROGRAMMING

• Intro to programming & python

• Datatypes

•  Operators

•  Expressions

• Conditionals

• Loops

• Functions

•  Recursion

•  OOPS Concepts

• Data Structures

• Numpy

• Pandas

• Error Handling

• Scikit-learn library

MODULE-2 : MATHEMATICS FOR DATA SCIENCE

• Linear Algebra

• Calculus

• Statistics & Probability

• Linear Regression & Outlier Rejection

• Optimization and its techniques/types

• Constrained and Unconstrained Optimization

• Basics of Mathematical Preliminaries

• Stochastic Search

• PSO and Genetic Algorithm

MODULE 3 : INTRODUCTION TO ML & DIMENSIONALITY REDUCTION

• Intorduction to Machine Learning

• Feature Selection

• Hashing

• Principal Component Analysis(PCA)

• Discriminant Functions

• Linear Discriminant Analysis(LDA)

• Exploratory Factor Analysis(EFA)

MODULE 4 : UNSUPERVISED LEARNING, GENERATIVE MODELS AND PATTERN DISCOVERY

• Introduction to Unsupervised Learning

• Spherical K-Means and Incremental Clustering

• K-Means Clustering

• Hierarchical Agglomerative Clustering and DBSCAN

• Mean-shift Clustering

• Estimation Theory

• PLSA & Sequence Mining

• Association Mining Rule

• Hidden Markov Model (HMM)

MODULE 5: SUPERVISED LEARNING AND DISCRIMINATIVE MODELS

• Introduction To Supervised Learning

• Bayesian Classification and Performance Analysis

• Logistics Regression

• Perceptron

• Multilayer Perceptron

• MLP: Backpropagation and Applications

• K-NN and RBF, ANN

• Data Balancing Techniques And Applications

• Classification and Regression Trees

• Decision Trees and Random Forest

• Support Vector Machines (SVM)

• Bootstraping and Boosting

MODULE 6: MACHINE LEARNING & CLOUD

• Cloud fundamentals

• ML tools on Cloud (AWS, Azure, GCP)

• Deploying ML project on cloud

MODULE 7: DEEP LEARNING

• Introduction to Deep Neural Network

• Convolutional Neural Network

• Implementation of ConvNet/CNN from scratch

•  Convolutional Neural Network (ConvNet/CNN)

•  Architectures ConvNet/CNN Demonstration

MODULE 8: SPEECH, VISION, NLP

• Image Processing using OpenCV

• Signal Processing

• SciPy

• Introduction to Natural Language Processing (NLP)

• Language Models

• Vector Semantics

• Neural Models for NLP

• NLP Libraries

MODULE 9: CAREER SERVICES & CAPSTONE PRESENTATION

• Resume Building

• Interview preparation workshop

• Capstone Project