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Machine Learning

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Machine Learning

About The Course

This comprehensive course provides a structured pathway to mastering Machine Learning (ML), starting from foundational concepts to advanced model deployment. It emphasizes both theoretical understanding and hands-on implementation using real-world datasets and industry tools such as Python, Scikit-learn, TensorFlow, and more.

What Will You Learn?

Core ML concepts and mathematical foundations

Data preprocessing and feature engineering

Supervised and unsupervised learning techniques

Model evaluation and optimization

Deep learning fundamentals

Real-world ML project building

Model deployment using APIs and cloud tools

The Course Curriculam

Module 1: Introduction to Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  • Real-world Applications of ML
  • ML Workflow Overview
  • Setting Up the Environment (Python, Jupyter, Colab)

Module 2: Python for Machine Learning

Module 3: Mathematics for ML (Essential Concepts)

Module 4: Data Preprocessing & Feature Engineering

Module 5: Supervised Learning – Regression

Module 6: Supervised Learning – Classification

Module 7: Unsupervised Learning

Module 8: Model Optimization & Tuning

Module 9: Introduction to Deep Learning

Module 10: Advanced Topics

Module 11: Model Deployment

Module 12: Capstone Projects

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