Artificial Intelligence Training in Chandigarh
Artificial Intelligence

Artificial Intelligence

Course Overview

Build a strong foundation in Artificial Intelligence and advance towards real-world AI applications. This program covers Machine Learning, Deep Learning, NLP, Computer Vision, and Generative AI, helping you develop intelligent systems used in modern industries.


 

Course Curriculum:

AI Foundations (Beginner Level)

Module1: Introduction to Artificial Intelligence
  • What is AI?
  • Types of AI (Narrow, General, Super AI)
  • AI vs ML vs Deep Learning
  • Real-world applications (Healthcare, Finance, E-commerce)
  • AI lifecycle overview
Module2: Python for AI
  • Python basics (variables, loops, functions)
  • Data structures (list, tuple, dictionary)
  • Libraries introduction:
  • NumPy
  • Pandas
  • Jupyter Notebook Usage
Module 3: Data Preprocessing & Analysis
  • Data collection & datasets
  • Data cleaning:
  • Missing values
  • Duplicates
  • Feature selection basics
  • Data transformation
  • Exploratory Data Analysis (EDA)
Module 4: Machine Learning Fundamentals
  • What is Machine Learning
  • Types:
  • Supervised
  • Unsupervised
  • ML workflow (Train → Test → Evaluate)
  • Overfitting vs Underfitting
  • Model evaluation basics
Module 5: Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • K-Nearest Neighbors (KNN)
Module 6: Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
  • Dimensionality Reduction (PCA basics)
Module 7: Introduction to Neural Networks
  • Biological vs Artificial Neural Networks
  • Perceptron concept
  • Activation functions
  • Basic neural network structure
Module 8: NLP Basics
  • What is NLP
  • Text preprocessing:
  • Tokenization
  • Stopwords removal
  • Bag of Words
  • TF-IDF
  • Sentiment Analysis (basic)

Advanced AI

Module 9: Deep Learning
  • Introduction to Deep Learning
  • Neural network training
  • Loss functions & optimizers
  • Introduction to frameworks:
  • TensorFlow
  • PyTorch
Module 10: Advanced Machine Learning
  • Gradient Boosting
  • XGBoost
  • LightGBM (intro)
  • Hyperparameter tuning
  • Feature engineering
Module 11: Natural Language Processing
  • Word Embeddings
  • Introduction to Transformers
  • Overview of BERT
  • Chatbot basics
  • Text classification
Module 12: Generative AI (NEW & IMPORTANT)
  • Introduction to Generative AI
  • Large Language Models (LLMs)
  • Working with ChatGPT
  • Prompt Engineering basics
  • Use cases (automation, content, coding)
Module 13: Computer Vision
  • Introduction to Image Processing
  • Image classification basics
  • Object detection overview
  • Use cases:
  • Face detection
  • Medical imaging
Module 14: Reinforcement Learning
  • What is Reinforcement Learning
  • Agent, environment, reward
  • Basic examples (game AI)
Module 15: AI Ethics & Responsible AI
  • Bias in AI
  • Fairness & transparency
  • Data privacy
  • Ethical AI usage
Module 16: AI Deployment & Applications
  • Model deployment basics
  • Introduction to APIs
  • Deploying models using:
  • Streamlit
  • Real-world AI applications
Module 17: Project Work
  • Final project presentation
  • Project documentation
  • Project evaluation