Machine Learning Tutorial

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

Module-I: Basics of Machine Learning

• Introduction to Machine Learning
• Data set - Types of data sets
• Steps to build a machine learning model
• Types of Machine Learning Techniques
• Goals and applications of machine learning
• Benefits of machine learning
• Challenges of machine learning

Module-II. Machine Learning Tools and Libraries

• Programming languages for machine learning
• Popular machine learning libraries
• Cloud-based machine learning platforms

Module-III. Supervised Learning

• What is supervised learning?
• Common supervised learning algorithms
• Regression
• Classification
• Evaluation metrics for supervised learning

Module-IV. Unsupervised Learning

• What is unsupervised learning?
• Common unsupervised learning algorithms
• Clustering
• Dimensionality reduction
• Anomaly detection

Module-V. Reinforcement Learning

• What is reinforcement learning?
• Common reinforcement learning algorithms
• Q-learning
• Policy gradients

Module-VI. Machine Learning Best Practices

• Data preparation
• Model selection
• Model evaluation
• Model deployment

Module-VII. Advanced Machine Learning Topics

• Deep learning
• Natural language processing
• Computer vision
• Recommender systems

UNIT-I

1.  Introduction to Machine Learning
   
2. Definition of learning systems
3. Goals and applications of machine learning
4. Aspects of developing a learning system
5. The concept learning task
6. Concept learning as search through a hypothesis space
7.  General-to-specific ordering of hypotheses
8. Finding maximally specific hypotheses ( Find S-Algorithm)
   
9. Version spaces and the candidate elimination algorithm
10. Learning conjunctive concepts
    
11. The importance of inductive bias 

UNIT-II (Decision Tree Learning)

1. Representing concepts as decision trees
2. Recursive induction of decision trees
   
3. Picking the best splitting attribute
4. entropy and information gain
   
5. Searching for simple trees and computational complexity
6. Occam's razor
7. Over fitting, noisy data, and pruning.
8. Experimental Evaluation of Learning Algorithms: 
9. Measuring the accuracy of learned hypotheses. 
10. Comparing learning algorithms:
11.  cross-validation
12. learning curves, and statistical hypothesis testing.

UNIT-III