Intermediate · Applied ML

Machine Learning Essentials

Learn how machines learn from data. Build predictive models, understand algorithms intuitively, and apply ML to real datasets — without needing a math degree.

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8–10

Weeks

ML projects

Hands-on

Model building

Overview

Practical ML — not just theory

Understanding algorithms, training models, and evaluating results you can actually explain.

ML fundamentals

How machines learn patterns from data — the intuition behind every algorithm you'll use.

Core algorithms

Regression, classification, and clustering — built from scratch and then with scikit-learn.

RegressionClassificationClustering

Outcome

Build and explain ML models confidently — and know when NOT to use them.

Curriculum

Concept → Model → Evaluation → Improvement

A progression that mirrors how data scientists actually work.

Module 1

ML Foundations

What is machine learning and when to use it
Supervised vs unsupervised learning
Training vs testing data — and why it matters

Module 2

Regression Models

Linear & multiple regression
Error metrics: MAE, RMSE, R²
Model assumptions and diagnostics

Module 3

Classification Models

Logistic regression, KNN, decision trees
Confusion matrix, accuracy, precision, recall
Choosing the right metric for your problem

Module 4

Model Evaluation

Overfitting, underfitting, and the bias-variance tradeoff
Train/test split and cross-validation basics
Hyperparameter tuning introduction

Module 5

Clustering & ML Pipelines

K-Means clustering and use cases
Feature scaling and preprocessing
End-to-end ML workflow with scikit-learn

What you'll submit

Model notebooks + evaluation reports — ready to show in interviews.

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Portfolio Projects

Real ML thinking, real portfolio impact

Projects that demonstrate you can work through a problem end to end.

House Price Prediction

Regression

Predict house prices using regression models trained on real estate data.

Feature selection & engineering
Error evaluation & comparison
Model interpretation

Customer Churn Model

Classification

Predict which customers are likely to leave using binary classification.

Logistic regression + decision tree
Confusion matrix analysis
Precision vs recall tradeoffs

Customer Segmentation

Clustering

Group customers by behavior using unsupervised learning to drive business decisions.

K-Means clustering
Feature scaling
Business interpretation & recommendations

Pricing

Advanced skills at accessible pricing

No compromise on quality — just made affordable.

Starter

₹7,999

Self-paced

Full curriculum
Model notebooks
Community access

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Mentored

₹14,999

Career-focused

Weekly mentor calls
Model code reviews
Interview prep

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Team

Custom

Institutes & companies

Custom curriculum
Progress tracking
Bulk pricing

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Enquire or get the syllabus

Ask about prerequisites, check if you're ready, or just request the full syllabus PDF.

Course: Machine Learning Essentials

Skills: Regression, Classification, Clustering, scikit-learn

Prereq: Python basics recommended