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Live Bootcamp · Seats Limited

4-Hour
ML Masterclass

Live session covering core algorithms, data pipelines & model evaluation — from absolute beginner to writing real ML code. ₹29 only.

📅17 May 2026
🕖7:00 PM – 11:00 PM IST
⏱️4 Hours Live
🐍Python
💰Only ₹29
View Syllabus
🤖7 Core ML Algorithms
🔄Full ML Pipeline
📊Model Evaluation Metrics
🧹Data Preprocessing
📄Jupyter Notebooks
🏆Certificate
Full Syllabus

10 Modules · 4 Hours · 1 Breakthrough

Every module is live-coded with real datasets. No slides-only fluff — you leave with working Python notebooks.

🧠
Hour 1
Foundations & Core Concepts
0–60 min
M1The Big Picture
AI → ML → Deep Learning hierarchy
Real-world apps: Netflix, fraud detection, medical diagnosis
Types of ML: Classification, Regression, Clustering
M2Learning Paradigms
Supervised Learning — labeled data, predict outcomes
Unsupervised Learning — find hidden patterns
Reinforcement Learning — reward-based learning
When to use which?
M3Types of Models & Algorithms
Parametric vs Non-parametric models
Quick visual taxonomy of algorithms
⚙️
Hour 2
Core Algorithms Deep Dive
60–120 min
M4Regression Algorithms
Linear Regression — line of best fit, cost function, gradient descent
Logistic Regression — sigmoid function, binary classification
Polynomial Regression — handling non-linear data
M5Tree-Based Models
Decision Tree — splitting criteria (Gini, Entropy), pruning
Random Forest — bagging, ensemble power, feature importance
Hands-on:Predict house prices · Classify Iris dataset
🔬
Hour 3
Unsupervised + Data Engineering
120–180 min
M6Unsupervised Algorithms
Clustering — K-Means, Elbow method for choosing K
Association Rules — Apriori, support / confidence / lift
Use cases: customer segmentation, market basket analysis
M7Data Preprocessing
Handling missing values & outliers
Feature encoding — Label, One-Hot
Feature scaling — Normalization vs Standardization
Train / Test / Validation split
Garbage in, garbage out — why data quality matters
Hands-on:Customer segmentation · Market basket analysis
📊
Hour 4
Training, Evaluation & Best Practices
180–240 min
M8Model Training
Fitting a model — hyperparameters vs parameters
Cross-validation — K-Fold
M9Evaluation Metrics
Confusion Matrix — TP, TN, FP, FN explained visually
Precision, Recall, F1-Score, Accuracy
ROC-AUC Curve
RMSE / MAE for regression
M10Overfitting & Underfitting
Bias-Variance Tradeoff — the core ML challenge
Solutions: Regularization (L1/L2), Dropout, More Data
Learning curves diagnosis
End-to-end ML pipeline recap + what to learn next
Your Instructor

Learn from someone who's built it for real.

Aditya Dubey
AD
Aditya Dubey
ML Engineer & Educator
“ML felt impossible until I stopped reading theory and started breaking models intentionally. That's what we'll do in this session.”
Aditya has built ML systems that process real-world data at scale. He's passionate about making machine learning accessible — not just the math, but the intuition behind why algorithms work (and when they don't). This session is everything he wishes he had when starting out.
Machine LearningPythonData ScienceModel EvaluationScikit-LearnPandas
4+Years in ML
10+Modules Covered
LiveHands-On Session
15,000+ Students Mentored
by Aditya Dubey across sessions & workshops
Register Now

Reserve your seat — ₹29 only

17 May 2026 · 7:00 PM – 11:00 PM IST · Limited seats

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What You Get
ML Masterclass
📅 17 May 2026 · 7:00 PM – 11:00 PM
🤖10 Modules across 4 Hours
⚙️7 Core ML Algorithms — Live Coded
🔄Full ML Pipeline Walkthrough
📊Confusion Matrix & Model Metrics
🧹Data Preprocessing Techniques
📄Session Recording + Jupyter Notebooks
🏆Completion Certificate