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# separating features and target
** Introduction to Machine Learning & AI with Python
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X = df.drop(“price”,axis=1)
y = df[[“price”]]
Course Outline
Section 1: Types of ML Models
6 lessons
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What is Machine Learning?
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How does AI work?
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Types of Machine Learning Models (Core Concepts)
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1) Regression — Predicting Numbers
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2) Classification — Predicting Categories
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3) Clustering — Finding Hidden Groups
Section 2: Predicting Diamond Prices with Machine Learning
10 lessons
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Scenario: Predicting Diamond Prices with Machine Learning
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ML Workflow Overview
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Step 1 - Data Collection
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Step 2 — Exploratory Data Analysis (EDA)
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Step 3 — Feature Engineering
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Step 4 — Build & Train the Model
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Step 5 — Evaluate Performance
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Step 6 — Fine Tuning the Model
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Summary — The Full Workflow at a Glance
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Key Takeaways for Students
Section 3: Exercise 2. Supervised Learning (Linear Regression)
6 lessons
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Regression Model Building Exercise
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Regression
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Setup Libraries
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Load diamond dataset
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Data Preparation and Model readiness
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Execute & Evaluate Regression Model
Section 4: Exercise 3. Compare Regression Model Building Exercise (Linear, Ridge, Lasso, Elastic Net)
11 lessons
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Compare Regression Model Building Exercise
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Setup Libraries (Lasso, Ridge etc.)
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Load diamond dataset
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Data Preparation and Model readiness
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Regularization
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Lasso Regression (L1 Regularization)
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Ridge Regression (L2 Regularization)
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Elastic Net (Ridge + Lasso combined)
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Simple Rule of Thumb
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Execute and Evaluate Regression Models
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Create Graph (mo4)
Section 5: Wrapping Up - From Learning to Doing
7 lessons
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What You’ve Learned
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The Big Picture (Connect Everything)
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Real-World Mini Examples
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Common Mistakes (Super Valuable Section)
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Your First Independent Project
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Where to Go Next
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Final Mindset Shift
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