Advance AI Course

Advance AI Course by Irfan Malik - In this repository, you will find:

• Handwritten Notes: My personal handwritten notes covering key topics, concepts, and explanations from the course.
• Slides of Lectures
• Youtube Playlist Link

---

Topics Covered

Lecture Slides of Lecture 1-41

1. Orientation Basic

• Lecture 1: Road map of AI
• Lecture 2: MAchine Learning Essentials
• Lecture 3: Discriminative AI vs Gen AI
• Lecture 4: Deep Dive into Generative AI
• Lecture 5: Prompt Engineering
• Lecture 6: Diffusion Models

Programming Concepts

• Lecture 7: Programming Fundamental
• Lecture 8: Basics of Python
• Lecture 9: Operators
• Lecture 10: Condition & Loops in Python Lecture 11: Loops & Functions

API & Hugging Face

• Lecture 12: intro to API
• Lecture 13: Intro to Hugging Face
• Lecture 14: Hugging Face Pipeline

Data Science

• Lecture 15: Data Science
• Lecture 16: Data Pre-processing

Machine Learning

• Lecture 17: Classification using Scikit Learn
• Lecture 18: Classification Algorithms
• Lecture 19: Regression & its Algorithm
• Lecture 20: Classification Evaluation Metrics
• Lecture 21: Evaluation Metrics

Deep Learning

• Lecture 22: Intro & Basics of Deep Learning
• Lecture 23: Basic of Deep Learning
• Lecture 24: Deep Learning
• Lecture 25: ANN concept & Training
• Lecture 26: Training of Simple Neural Network
• Lecture 27: CNN overview & working
• Lecture 28: CNN in Detail
• Lecture 29: Auto Encoders part1
• Lecture 30: Auto Encoders part2
• Lecture 31: Auto Encoders part3
• Lecture 32: RNN
• Lecture 33: Attention Model & Transformers
• Lecture 34: Implementation of RNN & LSTSMS
• Lecture 35: Transformers & GPT

RAG and Its Working

• Lecture 36: Intro to RAG Systems
• Lecture 37: Chunks & Embeddings
• Lecture 38: Embedding Models
• Lecture 39: Vector Stores
• Lecture 40: RAG Retrievers

In Summary

• Understanding of the fundamentals of artificial intelligence and its various applications.
• Familiarity with popular AI tools like ChatGPT, DALL-E, and Stable Diffusion.
• Proficiency in Python programming language and its data structures, control statements, functions, and classes.
• Knowledge of different types of machine learning, their applications, and the difference between supervised, unsupervised, semi-supervised, and reinforcement learning.
• Understanding of machine learning models, datasets, data preprocessing, training, testing, and evaluation metrics.
• Familiarity with different machine learning frameworks and their usage in creating structured data models.
• Knowledge of data visualization techniques using Matplotlib, Seaborn, and Plotly libraries.
• Familiarity with Hugging Face library and its usage in NLP tasks like text classification, NER, and sentiment analysis.