AI Skills Pay the Bills. Start Now
Step into the world of AI.
Master skills, create solutions, and shape the future.
Join Free Demo Class
Start Date
Seats Filled Next Batch Soon
Duration
2 months
Timings
Timings Will Be Updated
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)