PG Certificate in Data Science, Machine Learning & Generative AI – Batch 11

Exploratory Data Analysis and Data Visualisation

• Data extraction and cleaning
• Data visualisation using Python
• Data visualisation using Tableau
• Regular expressions
• RDBMS and SQL

Machine Learning

• ML Project cycle and Naïve Bayes classifier with Hands-on
• Regression analysis
• Logistic regression; SoftMax regression; One hot encoding
• SVM and KNN classifiers
• Clustering approaches
• Decision Trees

Advanced Machine Learning

• Ensemble learning: bagging and boosting models
• Linear and Non-linear dimensional reduction techniques
• Statistical/Probabilistic and Density methods for clustering
• Gaussian Mixture models for clustering and data generation

Text Analytics

• Basics of text analytics
• Text pre-processing, frequency-based analysis
• Converting text to numbers, building ML models from text data
• N-gram models, evaluating language models
• Word2Vec, applications of deep models to text data

Neural Networks & Deep Learning

• Introduction to ANN
• ANN through Scikit-learn
• Introduction to Keras and TensorFlow
• Sequential and Functional models
• Improving the performance of neural networks

MLOps

• Version Control Systems
• Containerization (Docker)
• CI/CD Pipelines (GitHub Actions)
• CI/CD pipeline: AWS
• CI/CD pipeline: Deployment

Generative AI

• Prompt fundamentals
• Building GenAI tools/ custom GPT
• Prompt techniques

Specialisation 1 - Deep Learning with Image and Speech with Generative AI

• Image: Introduction to computer vision, OpenCV, CNN
• Image: Introduction and Hands-on popular CNN architectures
• Image: Popular object detection architectures
• Image: Autoencoders, GANs, Diffusion models
• Speech: Processing sequential data using RNN
• Speech: Processing sequential data using LSTM
• Speech: Introduction to word embeddings and advanced NLP
• Introduction to Transformers architecture
• GenAI: RAG
• GenAI: AI agents and multi-agent systems
• GenAI: LangChain

Specialisation 2 - Data Engineering with Generative AI

• Introduction to Big Data and HDFS, Azure Data Lake Storage Gen2 & Blob, Distributed systems, replication & lifecycle management
• Apache Spark & RDD architecture, PySpark APIs, Spark SQL & data frame, aggregations & window functions, ETL pipelines
• Batch v/s Streaming architecture, Structured streaming in Spark, Kafka fundamentals, streaming data frames
• Feature engineering with Spark, MLib for feature pipelines
• ETL/ELT with Azure data factory, linked services & pipelines, triggers, serverless transformations
• AI-assisted data cleaning & transformations, schema/metadata generation, automated feature enrichment, missing value imputation, AI-powered ETL
• Complete Azure-based Data Engineering pipeline with Gen AI integration

Capstone Project

• Apply all learned concepts in a comprehensive real-world project.
• Showcase the ability to solve industry-relevant problems.
• Gain practical experience in managing and deploying a complete data science solution.