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Machine Learning Projects (9–12)

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About Course

A hands‑on course where students build real machine learning projects using datasets, tools, and guided frameworks.

What Will You Learn?

  • • How to prepare datasets
  • • How to train ML models
  • • How to test and improve accuracy
  • • How to visualize data
  • • How to build ML apps
  • • How to present ML findings

Course Content

Topic 1: ML Basics
Supervised vs. unsupervised learning.

Topic 2: Data Preparation
Cleaning, labeling, splitting datasets.

Topic 3: Model Training
Regression, classification, clustering.

Topic 4: Evaluation & Optimization
Accuracy, precision, recall, tuning.

Topic 5: ML Tools & Platforms
Beginner‑friendly ML environments.

Topic 6: Final Project — Build a Machine Learning App
Students create a working ML model and present results.

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