July 13th to Aug 31st, 2024 - 10 AM to 11:30 AM
Location: Om Sri Sai Balaji Temple @ Monroe NJ
- What is AI and ML?
- Real-world applications
- Basic AI terminology and concepts
- Types of learning: Supervised, Unsupervised
- Mathematical foundations: Linear Algebra: Vectors, Matrices, and Operations
- Probability and Statistics: Basics, Bayes Rule, Distributions, and Data Analysis
- Hand On: python environment setup.
- Concept and uses of linear regression
- The least squares method
- Use case and problem statement to set pretext on AI application.
- Hands-on activity: Implementing linear regression in Python
- Logistic regression vs. linear regression
- Sigmoid function and binary classification
- Hands-on activity: Implementing logistic regression in Python
- Supervised Learning: Examples and use cases
- Unsupervised Learning: Examples and use cases
- Key differences and examples of algorithms
- Hands on activity: Supervised and Unsupervised learning.
- Minor Project 1.
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Linear classifiers and perceptron’s
- Hands-on activity: Implementing KNN and Naive Bayes in Python
- Decision Tree basics and construction
- Random Forests: Introduction and importance of ensembles
- Hands-on activity: Implementing decision trees and random forests in Python
- Essential Python packages: NumPy, Pandas, Matplotlib, Scikit-learn,
- Hands-on activity: Basic ML model using Scikit-learn
- Introduction to GPT, Transformers, and BERT
- CNN, Dalle,
- Hands-on activity: UsingGPT for coding.