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| Item Details | Price | ||
|---|---|---|---|
Machine Learning Projects
Instructor: CampusX
Language: Hindi
Validity Period: Lifetime
This course is all about building real-world machine learning projects—start to finish. You’ll apply everything you’ve learned in practical scenarios that mimic actual industry work.
You’ll work on projects like:
- Real Estate Price Prediction
- Youtube Comment Section Analysis
- Swiggy Delivery Time Prediction
- Hybrid Recommender System
- Uber Demand Prediction
Each project includes:
- Problem understanding
- Data cleaning and exploration
- Model building and evaluation
- Feature engineering and tuning
- Final presentation or deployment
By the end, you’ll have a strong ML portfolio that shows what you can do—not just what you know.
Course Duration: 52+ hours
| Capstone Project 1 | Real Estate Price Prediction | |||
| Session 1 | Data Gathering 94:00 | |||
| Session 2 | Data Cleaning 48:00 | |||
| Session 3 | Feature Engineering 51:00 | |||
| Session 4 | EDA 115:00 | |||
| Session 5 | Outlier Detection and Removal 33:00 | |||
| Session 6 | Missing Value Imputation 36:00 | |||
| Session 7 | Feature Selection 89:00 | |||
| Session 8 | Model Selection & Productionalization 116:00 | |||
| Session 9 | Building the Analytics Module 98:00 | |||
| Session 10 | Building the Recommender System 93:00 | |||
| Session 11 | Building the Recommender System Part 2 73:00 | |||
| Session 12 | Building the Insights Module 76:00 | |||
| Session 13 | Deploying the application on AWS 86:00 | |||
| Capstone Project 2 | YT Comment Section Analysis | |||
| Session 1 - Project Planning 87:00 | |||
| Session 2 - Preprocessing & EDA 35:00 | |||
| Session 3 - Building a Baseline Model 51:00 | |||
| Session 4 (Part 1) - Improving the Baseline Model 27:00 | |||
| Session 4 (Part 2) - Improving the Baseline Model 36:00 | |||
| Session 5 - Improving the LightGBM model 52:00 | |||
| Session 6 - Building the DVC Pipeline 51:00 | |||
| Session 7 - Adding Model Registry 41:00 | |||
| Session 8 - Building the Chrome Plugin Part 1 64:00 | |||
| Session 9 - Building the Chrome Plugin Part 2 62:00 | |||
| Session 10 (Part 1) - Adding the CI workflow 31:00 | |||
| Session 10 (Part 2) - Adding the CI Workflow 35:00 | |||
| Session 11 - Dockerization 38:00 | |||
| Session 12 - Deployment 38:00 | |||
| Capstone Project 3 | Swiggy Delivery Time Prediction | |||
| Session 1 - Understanding the Problem Statement 36:00 | |||
| Session 2 - Data Cleaning 69:00 | |||
| Session 3 - EDA 71:00 | |||
| Session 4 - Building a baseline model 48:00 | |||
| Session 5 (Part 1) - Experimentation Part 1 45:00 | |||
| Session 5 (Part 2) - Experimentation Part 2 23:00 | |||
| Session 6 - Building the DVC Pipeline Workflow 60:00 | |||
| Session 7 - Model Registry and Building the API 50:00 | |||
| Session 8 (Part 1) - Model Testing 19:00 | |||
| Session 8 (Part 2) - CI 26:00 | |||
| Session 9 - Dockers and CD 39:00 | |||
| Session 10 (Part 1) - Deployment on AWS 31:00 | |||
| Session 10 (Part 2) - Deployment on AWS 20:00 | |||
| Capstone Project 4 | Hybrid Recommender System | |||
| Session 1 - Project Overview 74:00 | |||
| Session 2 - EDA 65:00 | |||
| Session 3 (Part 1) - Content Based Recommender System 36:00 | |||
| Session 3 (Part 2) - Content Based Recommender System 22:00 | |||
| Session 4 (Part 1) - Collaborative Filtering Based Recommender System 83:00 | |||
| Session 4 (Part 2) - Collaborative Filtering Based Recommender System 28:00 | |||
| Session 5 - Building the Hybrid Recommender System 50:00 | |||
| Session 6 - Improving Hybrid Recommender System 43:00 | |||
| Session 7 - DVC Pipeline & CI 36:00 | |||
| Session 8 - Dockers and CD 51:00 | |||
| Session 9 - Deployment on AWS 30:00 | |||
| Session 10 (Part 1) - Blue Green Deployment Phase 1 64:00 | |||
| Session 10 (Part 2) - Blue Green Deployment Phase 2 30:00 | |||
| Capstone Project 5 | Uber Demand Prediction | |||
| Session 1 - Project Overview 53:00 | |||
| Session 2 (Part 1) - EDA 40:00 | |||
| Session 2 (Part 2) - Demand Prediction EDA 41:00 | |||
| Session 3 - Breaking New York into regions 59:00 | |||
| Session 4 - Creating Historical Data 55:00 | |||
| Session 5 - Training a baseline model 33:00 | |||
| Session 6 - Model Selection and HP Tuning 32:00 | |||
| Session 7 - Building the DVC Pipeline 58:00 | |||
| Session 8 - Building the Streamlit Application 47:00 | |||
| Session 9 - Building the CI Pipeline 54:00 | |||
| Session 10 - Deployment using AWS Code Deploy 50:00 | |||
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