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Supervised Machine Learning Using Python

Artificial Intelligence(ai)

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30,000+ Learners

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Aim

This course teaches you how to build reliable supervised machine learning models using Python. You will learn the complete workflow—data preparation, feature engineering, model training, evaluation, tuning, and interpretation—using practical datasets and industry-standard tools (NumPy, Pandas, Scikit-learn). By the end, you’ll be able to choose the right model for a problem, validate performance correctly, and deploy-ready your pipeline for real use cases.

Program Objectives

Learn the End-to-End Workflow: Go from raw data to a tested, reproducible ML pipeline.
Master Core Algorithms: Train and compare regression and classification models with confidence.
Improve Model Performance: Apply feature engineering, scaling, and hyperparameter tuning.
Evaluate Correctly: Use proper metrics, cross-validation, and avoid data leakage.
Interpret Models: Understand feature importance and explain predictions responsibly.
Hands-on Project: Build a complete supervised ML project with a final report.

Program Structure

Module 1: Supervised ML Foundations

What supervised learning is (and when to use it).
Regression vs classification with real examples.
Key concepts: features, labels, training/testing, generalization.
Common mistakes: leakage, overfitting, and misleading accuracy.

Module 2: Data Preparation with Python

Reading datasets with Pandas and building clean data frames.
Handling missing values, outliers, and inconsistent data.
Encoding categorical variables (one-hot, ordinal).
Train/validation/test split and why it matters.

Module 3: Feature Engineering (Where Performance Comes From)

Scaling and normalization (when required and why).
Creating useful features from dates, text-like fields, and grouped data.
Feature selection basics: correlation checks and simple selection strategies.
Pipelines: making preprocessing + modeling reproducible.

Module 4: Regression Models in Practice

Linear Regression and regularization (Ridge/Lasso).
Tree-based regression (Decision Trees, Random Forests).
Gradient Boosting basics (concept + practical workflow).
Regression metrics: MAE, MSE/RMSE, R² (how to interpret them).

Module 5: Classification Models in Practice

Logistic Regression, KNN, and Naive Bayes (use cases and limitations).
Decision Trees, Random Forests, and boosting for classification.
Classification metrics: precision, recall, F1-score, ROC-AUC.
Confusion matrix interpretation and threshold tuning.

Module 6: Model Validation & Tuning

Cross-validation: what it is and how it prevents false confidence.
Hyperparameter tuning: GridSearchCV and RandomizedSearchCV.
Handling imbalance: class weights, resampling concepts, metric choice.
Bias-variance tradeoff (in practical decision-making terms).

Module 7: Model Interpretability & Responsible ML

Feature importance and permutation importance (how to use them).
Interpretation for linear vs tree models (strengths and caveats).
Error analysis: where the model fails and why.
Responsible ML: fairness basics, data privacy, and avoiding overclaiming.

Module 8: Building an ML Pipeline (Project-Ready Workflow)

Creating end-to-end Scikit-learn pipelines.
Saving models and reproducible experiments (basic versioning concepts).
Simple deployment readiness: input validation and consistent preprocessing.
How to present results: clear reporting with metrics and visuals.

Final Project

Build a complete supervised ML solution for a real dataset (choose one track):
Track A (Regression): Predict a continuous outcome (e.g., house price, energy consumption).
Track B (Classification): Predict a category (e.g., churn, disease risk, fraud detection).
Deliverables: cleaned dataset, pipeline, model comparison, tuned best model, and final report.

Participant Eligibility

Beginners in ML with basic Python knowledge (loops, functions, data types)
Students in engineering, science, business analytics, or life sciences
Working professionals shifting into data science or analytics roles
Researchers who want to apply ML to structured datasets
Anyone aiming to build solid fundamentals in supervised learning

Program Outcomes

Workflow Mastery: Ability to build a complete supervised ML pipeline in Python.
Algorithm Confidence: Choose and train models for regression/classification problems correctly.
Strong Evaluation Skills: Use the right metrics and validation approach with less guesswork.
Performance Improvement: Apply feature engineering and tuning to improve results.
Portfolio Project: A complete ML project you can showcase in interviews or academic work.

Program Deliverables

Access to e-LMS: Full access to course content, notebooks, and practice datasets.
Hands-on Assignments: Practical exercises after each module to build confidence.
Project Guidance: Support and feedback for your final project.
Reusable Templates: Data cleaning checklist, ML pipeline template, evaluation report format.
Final Assessment: Certification after completion of assignments + final project submission.
e-Certification and e-Marksheet: Digital credentials provided upon successful completion.

Future Career Prospects

Machine Learning Engineer (Entry-Level)
Data Analyst / Data Scientist (ML-focused)
AI/ML Associate (Business, HealthTech, AgriTech, FinTech)
Predictive Analytics Specialist
Research Assistant (Data Science / ML)

Job Opportunities

Startups: Building predictive models for products and decision systems.
Enterprises: Analytics teams working on forecasting, risk models, and automation.
Healthcare & Bioinformatics: Clinical prediction, diagnostics support, and research analytics.
Finance & FinTech: Credit scoring, fraud detection, and risk analytics.
Retail & E-commerce: Demand forecasting, churn prediction, and customer analytics.

Category	E-LMS, E-LMS+Video, E-LMS+Video+Live Lectures

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Artificial Intelligence(ai)

Supervised Machine Learning Using Python

Aim

Program Objectives