AI/ML Free course Module-2.1 | ML Intuition, code, types in detail with real-life examples

In this video, we start *Module 2.1: Machine Learning* of our AI/ML course.

I explain from absolute basics and full intuition in HINDI as well as you can change the voice to ENGLISH:
What is Machine Learning? Human learning vs Machine Learning
Real-life intuition with house price prediction example
Traditional Programming vs Machine Learning (rules vs learning from data)
Basic math view: input (X), output (Y), model f(x, θ), parameters (W, b)
Loss function, error, Mean Squared Error (MSE)
Core idea of training: finding θ that minimises average loss
Complete high-level **ML pipeline**:
Problem definition → Data collection → Data cleaning → Feature engineering → Model selection → Training → Evaluation → Deployment & monitoring
Types of Machine Learning:
Supervised Learning (Regression & Classification)
Unsupervised Learning (Clustering, PCA intuition, K-Means example)
Reinforcement Learning (agent, environment, state, action, reward)

We also see simple *Python + scikit-learn code* examples for:
Linear Regression (house price prediction)
Logistic Regression (classification)
K-Means clustering

This video is part of my *free AI/ML Crash Course* on YouTube, from beginner to advanced, completely in Hindi/English with strong mathematical intuition and real-world examples.

👉 Make sure to:
Pause and make your own notes
Try to run the Python code yourself

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