Programme Parameters
Advanced Postgrad
3 Days (4.5 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
This three-day course covers essential AI and ML concepts, deep learning fundamentals, and hands-on model development using Python. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This three-day course covers essential AI and ML concepts, deep learning fundamentals, and hands-on model development using Python.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• 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 Machine Learning Can and Cannot Do
• Supervised, unsupervised and reinforcement learning and the problems each suits
• Recognising a problem that does not need machine learning at all
• Data requirements and the label cost that decides most projects
Building a First Model in Python
• NumPy, pandas and scikit-learn as the working toolchain
• Train, validation and test splits, and the leakage that inflates every beginner result
• Cross-validation and the difference between tuning and evaluating
The Models Worth Knowing First
• Linear and logistic regression as interpretable baselines that are hard to beat
• Decision trees, random forests and gradient boosting on tabular data
• k-means and PCA for structure and dimensionality reduction
Measuring Honestly
• Accuracy, precision, recall, F1 and ROC AUC and when each misleads
• Class imbalance and why accuracy is meaningless for a rare outcome
• Overfitting, underfitting and reading a learning curve
Neural Networks in Outline
• Perceptrons, backpropagation and gradient descent conceptually
• CNNs for images and transformers for sequences, and when each is warranted
• Transfer learning as the practical route when data is limited
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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