Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Supervised Machine Learning Using Python gives you a thorough, practical command of the most widely used branch of ML: learning from labelled data. You work through the core algorithms — linear and logistic regression, decision trees, random forests, support vector machines and gradient boosting — building each in Python with scikit-learn. Crucially, the course teaches the discipline around the models: proper train/test splits, cross-validation, handling imbalanced data, and tuning with grid and randomised search. You finish able to take a labelled dataset and deliver a well-evaluated classification or regression model. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches supervised machine learning in Python — regression and classification algorithms, model evaluation and tuning — built end to end with scikit-learn.
📋 Course Objectives
1. Build regression and classification models with scikit-learn.
2. Apply decision trees, random forests, SVMs and gradient boosting.
3. Evaluate models with cross-validation and the right metrics.
4. Handle imbalanced data and avoid overfitting.
5. Tune hyperparameters with grid and randomised search.
👥 Who Should Enroll?
• Beginners and analysts entering machine learning
• Developers adding predictive modelling
• Researchers applying ML to labelled data
• Students specialising in applied ML
🚀 Key Learning Outcomes
• The ability to build and evaluate a supervised model end to end.
• A classification or regression project for your portfolio.
• Sound judgement in model selection and validation.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Supervised Machine Learning Foundations
Apply linear algebra concepts to solve systems of linear equations and perform matrix operations • Analyze probability distributions and statistical measures to understand data characteristics • Develop mathematical models to represent real-world problems using supervised learning techniques
Data Engineering, Preprocessing, and Feature Pipelines
Design data pipelines to handle large datasets and perform data preprocessing tasks • Implement data normalization and feature scaling techniques to improve model performance • Configure data storage solutions to manage and retrieve data efficiently
Model Architecture, Algorithm Design, and Supervised Machine Learning Methods
Evaluate different supervised learning algorithms and their applications • Develop neural network architectures to solve complex classification and regression problems • Optimize model hyperparameters using grid search and random search techniques
Training, Hyperparameter Optimization, and Evaluation
Train supervised learning models using stochastic gradient descent and batch gradient descent • Analyze model performance using metrics such as accuracy, precision, and recall • Implement cross-validation techniques to evaluate model generalizability
Deployment, MLOps, and Production Workflows
Deploy supervised learning models using containerization and orchestration tools • Configure model serving pipelines to handle real-time predictions • Develop monitoring and logging systems to track model performance
Ethics, Bias Mitigation, and Responsible AI Practices
Identify and mitigate biases in datasets and models using fairness metrics • Develop strategies to ensure transparency and explainability in AI systems • Evaluate the ethical implications of AI systems and develop guidelines for responsible AI development
Industry Integration, Business Applications, and Case Studies
Apply supervised learning techniques to solve real-world problems in industries such as healthcare and finance • Analyze case studies of successful AI implementations and their impact on business outcomes • Develop strategies to integrate AI systems with existing business processes and infrastructure
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | scikit-learn |
| Covered Tool / Platform | pandas |
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