Programme Parameters
Advanced Postgrad
6 Weeks (60 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Machine Learning and AI Fundamentals course offers a comprehensive introduction to the core principles of machine learning and artificial intelligence. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Machine Learning and AI Fundamentals course offers a comprehensive introduction to the core principles of machine learning and artificial intelligence.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Assemble a documented case study that evidences your applied capability.
👥 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
🚀 Key Learning Outcomes
• 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
What Learning From Data Means
• Supervised, unsupervised and reinforcement learning distinguished by problem shape
• Features, labels, training and inference as a workflow
• Where machine learning is the wrong tool for the problem
The Working Set
• Linear and logistic regression and reading their coefficients
• Decision trees, random forests and gradient boosting
• k-means and hierarchical clustering for exploratory grouping
Knowing Whether It Works
• Train, validation and test splits and why the test set is touched once
• Accuracy, precision, recall, F1 and choosing by consequence
• Overfitting and underfitting diagnosed from learning curves
Preparation That Determines Outcomes
• Missing values, encoding and scaling done inside a pipeline
• Class imbalance and its effect on naive metrics
• Leakage: the failure that makes a bad model look excellent
A First End-to-End Project
• Framing a problem and selecting a metric before modelling
• Building, evaluating and iterating on a baseline
• Communicating results and known limitations
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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