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Why Artificial Intelligence?
✔️ Shaping the Future
AI powers self-driving cars, virtual assistants, and healthcare innovations, transforming how the world works.
✔️ Massive Career Opportunities
AI engineers, data scientists, and ML experts are among the most in-demand jobs worldwide
✔️ High-Paying Roles
AI professionals enjoy top-tier salaries with rapid career growth in multiple industries.
✔️ Adopted by Global Leaders
Companies like Google, Microsoft, Tesla, and Meta use AI to drive innovation and efficiency.
Course Structure
➤ Introduction to AI
➤ What is AI? Narrow AI vs General AI
➤ Difference between AI, ML, Deep Learning, Data Science
➤ Real-world applications (chatbots, recommendation systems, self-driving cars)
➤ Basics Of Linear Algebra
➤ Basics Of Probability & Statistics
➤ Basics Of Calculus
➤ Python basics (variables, loops, functions, OOP basics)
➤ Working with NumPy (arrays, vectorized operations)
➤ Working with Pandas (data frames, data cleaning, preprocessing)
➤ What is Feature Engineering?
➤ Why raw data isn’t enough
➤ Handling Numeric Data
➤ Scaling (Min–Max, Standardization)
➤ Simple transformations (log, square root)
➤ Handling Categorical Data
➤ Label Encoding
➤ One-Hot Encoding
➤ What Is Machine Learning?
➤ Definition of ML
➤ Types of ML: Supervised, Unsupervised, Reinforcement
➤ What Is Supervised Learning?
➤ Linear Regression
➤ Polynomial Regression
➤ Classification Algorithms
➤ Logistic Regression
➤ K-Nearest Neighbors (KNN)
➤ Support Vector Machines (SVM)
➤ Decision Trees
➤ Random Forests
➤ Metrics for regression: MSE, RMSE, MAE
➤ What is Unsupervised Learning?
➤ What is Clustering?
➤ K-Means Clustering
➤ Hierarchical Clustering
➤ DBSCAN
➤ Principal Component Analysis (PCA)
➤ What is Reinforcement Learning?
➤ Difference from supervised & unsupervised learning
➤ Agent, Environment, Action, State, Reward
➤ Q-Learning (learning from trial and error)
➤ SARSA (on-policy learning)
➤ Why Advanced Concept?
➤ What is Natural Language Processing(NLP)?
➤ What is Large Language Model(LLM)?
➤ What is Computer Vision(CV)?
➤ Basic Of NLP
➤ Text Preprocessing
➤ Tokenization
➤ Stemming
➤ Stop words
➤ Converting text to numbers
➤ Bag of Words (BoW)
➤ TF-IDF
➤ Basics Of LLM
➤ What is a Large Language Model (LLM)?
➤ Why are LLMs important in modern AI?
➤ Difference between Classical NLP vs LLM-based NLP
➤ Introduction to Computer Vision
➤ Image Fundamentals
➤ Pixels, channels (RGB, grayscale)
➤ Image transformations (resize, crop, rotate, flip)
➤ Image representation as arrays (NumPy)
➤ Edge detection (Sobel, Canny)
➤ Thresholding (binary images)