📚 Fundamentals
Lecture 1
Introduction to Machine Learning
Comprehensive overview of ML concepts, types, and applications
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Lecture 2
Classification vs Regression
Understanding the fundamental difference between prediction types
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Lecture 3
Supervised Learning Algorithms
Deep dive into supervised learning methods and techniques
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🔧 Core Algorithms
Lecture 4
Linear Regression Deep Dive
Mathematical foundations and practical implementation
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🧠Neural Networks & Deep Learning
Lecture 9
Neural Networks Basics
Introduction to artificial neural networks and backpropagation
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Lecture 11
Convolutional Neural Networks (CNN)
Deep learning for image recognition and computer vision
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Lecture 12
CNN Kernels & Filters
Deep dive into convolution mechanics, stride, padding, and terminology
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📊 Practical & Evaluation
Lecture 10
Model Evaluation & Metrics
Performance measurement and model validation techniques
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Special
Data Preprocessing
Data cleaning, feature scaling, and preparation techniques
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Special
Feature Selection
Selecting the most relevant features for better model performance
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🚀 Learning Path Recommendations
Beginner Path: Lectures 1 → 2 → 3 → 4 → 10
Algorithm Focus: Lectures 4 → 5 → 6 → 7 → 8
Deep Learning Path: Lectures 9 → 11
Practical Focus: Data Preprocessing → Feature Selection → Evaluation Metrics