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
The Advanced Machine Learning course is designed for those who wish to deepen their understanding of sophisticated machine learning techniques and their real-world applications. This course covers advanced topics such as ensemble methods, deep reinforcement learning, adversarial training, and transfer learning. Across 4 Weeks, you will build practical fluency in ensemble methods, deep reinforcement learning, and adversarial training, then consolidate everything in a capstone project. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Advanced Machine Learning course is designed for those who wish to deepen their understanding of sophisticated machine learning techniques and their real-world applications. This course covers advanced topics such as ensemble methods, deep reinforcement learning, adversarial training, and transfer learning.
📋 Course Objectives
1. Build practical fluency in ensemble methods.
2. Gain working command of deep reinforcement learning.
3. Develop hands-on skill in adversarial training.
4. Translate biotechnology theory into practical, reproducible analysis.
5. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into ensemble methods
🚀 Key Learning Outcomes
• Confidence to reason about ensemble methods in real projects.
• Confidence to apply deep reinforcement learning in real projects.
• Confidence to implement adversarial training in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• 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
Generalisation and Model Capacity
• Bias-variance decomposition and its limits as an explanation
• Regularisation viewed as constraint and as prior
• Why over-parameterised models generalise better than classical theory predicts
Combining Models Effectively
• Bagging, boosting and stacking, and the errors each reduces
• Gradient boosting internals and hyperparameter interaction
• Ensemble diversity and when combination stops helping
Uncertainty and Bayesian Methods
• Bayesian inference, priors and posterior predictive checks
• Gaussian processes and their scaling constraints
• Calibration, conformal prediction and reliable uncertainty intervals
Beyond Correlation
• Potential outcomes, confounding and identification assumptions
• Uplift modelling and heterogeneous treatment effects
• Why a predictive model cannot answer an interventional question
Models That Survive Deployment
• Distribution shift: covariate, label and concept
• Adversarial examples and robustness-accuracy trade-offs
• Monitoring, retraining triggers and safe degradation
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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