Module 1

Introduction to Data Science and Machine Learning: Machine Learning and its types, The Data Science Lifecycle. 

Module 2

Python Programming and Libraries: Basics of Python programming, Essential libraries: NumPy, Pandas, Matplotlib, Seaborn. 

Module 3

Mathematics and Statistics for Data Science: Linear algebra, calculus, and probability, Descriptive and inferential statistics. 

Module 4

Data Preprocessing and Exploratory Data Analysis (EDA): Data cleaning, handling missing values, and outliers, Exploring and visualizing data. 

Module 5

Supervised Learning: Regression (linear, polynomial, etc.), Classification algorithms (Decision Trees, Random Forest, SVM, etc.). 

Module 6

Self-Guided Learning: Clustering (K-Means, Hierarchical, etc.), Dimensionality reduction (PCA, t-SNE). 

Module 7

Model Evaluation and Hyperparameter Tuning: Cross-validation, bias-variance trade-off, Grid search, random search for hyperparameters. 

Module 8

Deep Learning Frameworks: TensorFlow and PyTorch, CNNs, RNNs. 

Module 9

Natural Language Processing (NLP): Sequential data analysis, text preprocessing, Advanced NLP: Word embeddings (Word2Vec, GloVe). 

Module 10

Overview of MLOps and its importance, Building a production pipeline for models. Model Deployment and Serving 

Module 11

Continuous Integration and Continuous Deployment (CI/CD): Setting up CI/CD pipelines for ML models, Monitoring and Scaling. 

Module 12

Capstone Project: Apply knowledge from Data Science, Machine Learning, NLP, Computer Vision, and MLOps to a real-world project.