All 9 specializations

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Each specialization is a line and each course a station: 9 lines, 85 stations. Start anywhere, change lines where they meet.

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01DS Programming9 02Machine Learning11 04Forecasting9 05Industry Projects15 03MLOps12 06Deep Learning6 07Generative AI10 08Optimization4 09Supplementary9 DS 01Foundations of Data Science (AI/ML): The Big PictureFree DS 02Core Python Programming (Beginner)Free DS 03Numpy for Data Science DS 04Pandas for Data Science DS 05SQL for Data Science - Level I (Beginner)Change for Supplementary DS 06SQL for Data Science - Level II DS 07SQL for Data Science - Level III DS 08SQL for Data Science - Window Functions DS 09High Performance Computing (Python)Change for Machine Learning Supplementary 01Spacy for NLP Supplementary 02Base R Programming Supplementary 03Dplyr for data wrangling Supplementary 04Wrangling data with Data.Table Supplementary 05GGPlot2 visualization for data analysis Supplementary 06Statistical Foundations for ML in R Supplementary 07Regression modeling in R Supplementary 08Caret package in R Supplementary 09Linux Command LineFree ML 01Linear Algebra for Machine Learning ML 02Statistics for Data Science ML 03Data Pre-processing and EDA ML 04Linear Regression and Regularisation ML 05Classification: Logistic Regression ML 06Imbalanced ClassificationChange for Optimization ML 07Supervised ML Algorithms ML 08Ensemble Learning ML 09Fixed and Mixed Effects Modelling ML 10Recommendation System - Deep Dive ML 11Explaining Machine Learning Models - Deep DiveChange for MLOps, Forecasting and Deep Learning Optimization 01Introduction to Optimization: Linear Programming Optimization 02Project: Inventory Planning and Holding Cost Optimization Optimization 03Optimization Part 2: Integer Programming Optimization 04Non Linear Optimization Forecasting 01Introduction to Time Series Analysis Forecasting 02Time Series Analysis - I (Beginners) Forecasting 03Time Series Analysis - II (Intermediate) Forecasting 04Time Series Forecasting Part 1 - Statistical Models Forecasting 05Time Series Forecasting Part 2 - ARIMA Modeling and Tests Forecasting 06Time Series Forecasting Part 3 - Vector Auto Regression Methods Forecasting 07Time Series Analysis - III: Singular Spectrum Analysis (SSA) Forecasting 08Feature Engineering for Time Series Projects - I Forecasting 09Feature Engineering for Time Series Projects - IIChange for Industry Projects Projects 01Portfolio Project: Estimating Customer Lifetime Value for Business CLTV Projects 02Portfolio Project: Microsoft Malware Detection Project Projects 03Portfolio Project: Credit Card Fraud Detection for Vesta Projects 04Portfolio Project: Restaurant Visitor Forecasting for Recruit Projects 05Portfolio Project: Optimizing marketing spend using Market Mix Modeling (MMM) Projects 06Portfolio Project: Predict Rating given Amazon Product Reviews using NLP Projects 07Uplift modeling: Estimating incremental impact of Marketing Campaigns Projects 08Uplift Modeling Part 2: Modeling Strategies Projects 09Survival Analysis: Predicting Time to Event in real world applications Projects 10Survival Analysis Part 2: Predicting Time to Event for Lungs Cancer Patients Projects 11Attribution Models in Marketing Projects 12Dynamic pricing using Multi Armed Bandit (Reinforcement Learning) Projects 13Reinforcement learning for Online Ad Serving with Multi Armed Bandits Projects 14Portfolio Optimization of volatile assets: Efficient Frontier (Python) Projects 15Conjoint Analysis - Project Deep Dive MLOps 01Docker for Data Science MLOps 02ML Deployment in AWS EC2 MLOps 03Deploy in AWS Lambda MLOps 04Deploy in AWS Sagemaker MLOps 05PySpark for Data Science - I: Fundamentals MLOps 06Automating AWS with Boto3 MLOps 07Automating AWS with Boto3 Part 2: Deploying PyTorch Models MLOps 08PySpark for Data Science - II: Statistics for Big Data MLOps 09PySpark for Data Science - III: Data Cleaning and Analysis Methods MLOps 10PySpark for Data Science - IV: Machine Learning MLOps 11PySpark for Data Science - V: ML Pipelines MLOps 12MLFlow in Action: Hands on guide to ML experiments Deep Learning 01Foundations of Deep Learning in Python Deep Learning 02Foundation of Deep Learning - Part 2 Deep Learning 03Applied Deep Learning with PyTorch Deep Learning 04Detecting Defects in Steel Sheets with Computer Vision Deep Learning 05Project: Text Generation using Language Models with LSTM Deep Learning 06Project: Classifying Sentiment of Reviews using BERT (NLP)Change for Generative AI GenAI 01Mastering Langchain: Part 1 GenAI 02LangGraph 101 GenAI 03LangGraph 201 GenAI 04Retrieval Augmented Generation (RAG) Techniques GenAI 05Productionizing RAG System GenAI 06Building Agentic RAG GenAI 07Building a Complete Agentic AI App - Advanced GenAI 08Model Context Protocol (MCP) GenAI 09Building LLM Apps with AWS Bedrock GenAI 10Building GraphRAG From Scratch: A Hands-On Walkthrough
9 lines, 85 courses. Open a station to see its course.Hover over a line or a station to follow it

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