Learning Path4

Learning Path:

Learning from Examples:
- Resources:
  - Machine Learning - Stanford University
  - [Pattern Recognition and Machine Learning by Christopher Bishop]
  - [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 18-19)]
- Notes: Different forms of learning, algorithms, and practical examples.
Supervised and Unsupervised Learning:
- Resources:
  - Supervised vs Unsupervised Learning - Medium
- Notes: Definitions, key differences, and example algorithms.
Learning Decision Trees:
- Resources:
  - Decision Trees - Scikit-Learn Documentation
- Notes: Decision tree algorithms, applications, and limitations.
Regression and Classification with Linear Model:
- Resources:
  - Regression and Classification - Towards Data Science
- Notes: Linear regression, logistic regression, and practical examples.
Support Vector Machines (SVM):
- Resources:
  - SVM - Scikit-Learn Documentation
- Notes: Define SVM, applications, and examples.
Ensemble Learning:
- Resources:
  - Ensemble Methods - Towards Data Science
- Notes: Types of ensemble methods, advantages, and examples.
Reinforcement Learning:
- Resources:
  - Reinforcement Learning - Coursera
- Notes: Key concepts, algorithms, and applications.
Artificial Neural Network:
- Resources:
  - Deep Learning - Coursera
  - [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville]
- Notes: Structure of ANN, types of neural networks, and applications.

Link to original note: AI-Learning ResourcesAI-Learning ResourcesTo help you gain a deep understanding of Artificial Intelligence (AI), we'll create a structured learning path based on the provided syllabus. The plan includes a detailed breakdown of each unit, along with recommendations for resources such as research papers, blogs, articles, videos, and other multimedia content. Additionally, we'll suggest methods for annotating and making notes to optimize your learning experience. Unit I - Introduction to AI * Topics1 * Learning Path1 * Multimedia Content