Programme Parameters
Foundation
3 Weeks (30 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Applied ML Fundamentals is a hands-on introduction to machine learning for people who want to build working models without wading through dense mathematics. You will learn how the main families of algorithms actually behave, when to reach for each, and how to train, evaluate and improve a model in Python with scikit-learn. The emphasis is judgement and practice: real datasets, honest evaluation metrics, and the habits that avoid pitfalls like overfitting and data leakage. You finish able to take a raw dataset and ship a defensible predictive model. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Applied ML Fundamentals teaches you to build, evaluate and improve real machine-learning models using Python and scikit-learn, with intuition and practice in place of dense theory.
📋 Course Objectives
1. Understand how regression, tree-based and clustering algorithms work in plain terms.
2. Clean, split and prepare real datasets for reliable training.
3. Train and tune models with scikit-learn without heavy mathematics.
4. Choose the right evaluation metric and diagnose overfitting and leakage.
5. Package a model into a simple, reproducible workflow.
👥 Who Should Enroll?
• Beginners and career-switchers entering ML without a maths background
• Analysts and developers who want practical ML skills quickly
• Students in any discipline needing applied ML for projects
• Domain professionals adding data-driven decisions to their work
🚀 Key Learning Outcomes
• Confidence to build and evaluate a predictive model end to end.
• A working ML mini-project you can show in a portfolio.
• The judgement to match the right algorithm to a problem.
• 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 a Model Actually Learns
• Features, labels and the supervised/unsupervised split in plain, worked terms
• The train/validation/test split and why a single accuracy figure misleads
• Data leakage — the single most common reason a "great" model fails in production
The Work That Decides the Result
• Cleaning, encoding categoricals and scaling features with pandas and scikit-learn
• Handling missing values and class imbalance without inventing signal
• Building a scikit-learn Pipeline so preprocessing cannot leak from test into train
The Core Models in Plain Language
• Linear and logistic regression as the interpretable baseline to always beat first
• Decision trees and random forests, and the overfitting a single deep tree invites
• k-means clustering for unlabelled data and the k-selection trap
Knowing Whether It Works
• Accuracy versus precision, recall, F1 and ROC-AUC — matching the metric to the problem
• Cross-validation instead of trusting one lucky split
• Diagnosing overfitting and underfitting from the gap between train and validation scores
From Notebook to Something Usable
• Persisting a trained model with joblib and loading it for repeatable inference
• Wrapping the model in a simple prediction service others can call
• Watching for input drift so a model that was accurate stays accurate
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