Module 1: Basic Python
 Variables
 Strings
 Lists
 Sets
 Tuples
 Dictionary
 Functions
 Classes
 If condition
 for and while loop
 Exception handling

Module 2: Data Handling and Visualization Python
 Pandas
 Matplotlib
 Seaborn

Module 3: Statistics and Probability
 Random variables
 Sampling
 Binomial distribution
 Poisson distribution
 Normal Distribution
 Exponential distribution
 Uniform distribution
 Descriptive statistics
 Central Limit theorem
 P-value
 Hypothesis testing

Module 4 : Linear Algebra
 Matrices and Vectors
 Addition and Scalar Multiplication
 Matrix Vector Multiplication
 Matrix Matrix Multiplication
 Matrix Multiplication Properties
 Inverse and Transpose
 Rank of a Matrix
 Eigen Vectors and Eigen Values

Module 5: Machine Learning Techniques
 Supervised Learning
 Unsupervised Learning
 Reinforced learning

Module 6: Regression Algorithms with case studies
 Linear Regression
 Polynomial Regression
 Ridge, Lasso, Elastic net Regressions
 Gradient Descent

Module 7: Classification Algorithms with case studies
 Logistic Regression
 Decision Tree
 Random Forest
 Support Vector Machines
 Naïve Bayes

Module 8: Clustering Algorithms with case studies
 K-Means
 DBSCAN
 Hierarchical

Module 9: Dimensionality Reduction with case studies
 Principal Component Analysis

Module 10: Boosting algorithms with case studies
 AdaBoost
 Gradient Boosting
 XgBoost
 CataBoost

Module 11 : Time Series Models
 Auto Regression Model
 Moving Average Model
 ARMA and ARIMA Models

Module 12: Project Handling
 Data retrieval
 Exploratory Data Analysis (Univariate, Bivariate and Multivariate)
 Data Wrangling
 Handling imbalanced data using SMOTE
 Model Building
 Hyper parameters
 Deployment methods

Module 13: End to End Projects (3 Projects) including deployment

Module 14: Fundamentals of MLOPS
 Google Cloud MLoPS Architecture
 Training and deploying models using AutoML
 Customized training and deploying models

Module 15: Fundamentals of Neural Networks
 Neural Network concepts
 Tensorflow and Keras libraries
 Back propagation Algorithm
 ANN, CNN, RNN with case studies