Computer Science Engineering

AI - Artificial Intelligence Course

Master the future of technology with AI and Machine Learning. Learn neural networks, deep learning, computer vision, NLP, and build intelligent systems that power ChatGPT, self-driving cars, and recommendation engines.

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Course Overview

Artificial Intelligence is revolutionizing every industry - from healthcare (disease diagnosis) to finance (fraud detection), autonomous vehicles (Tesla Autopilot) to entertainment (Netflix recommendations), and customer service (ChatGPT) to manufacturing (predictive maintenance). Our comprehensive AI course takes you from fundamental concepts to advanced deep learning, covering machine learning algorithms, neural networks, computer vision, natural language processing (NLP), and generative AI.

This course emphasizes practical implementation using Python and industry-standard frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn. You'll learn supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), reinforcement learning, convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequence data, transformers for NLP, and generative models (GANs, Stable Diffusion). The curriculum covers data preprocessing, model training, hyperparameter tuning, and deployment.

Taught by AI researchers and data scientists with expertise in building production-ready AI systems for Fortune 500 companies and startups, you'll master the mathematics (linear algebra, calculus, statistics), programming skills, and architectural patterns needed to become an AI/ML engineer. The hands-on approach ensures you build real AI applications - chatbots, image classifiers, recommendation systems, sentiment analyzers, and predictive models - preparing you for high-demand roles in AI/ML engineering, data science, and research.

Why Choose Our AI Training?

Industry-leading AI/ML training with real-world projects and cutting-edge technologies

AI/ML Researchers

Learn from AI scientists with PhDs and 10+ years building production AI systems for tech giants.

GPU-Powered Labs

Access to cloud GPUs (Google Colab, AWS) for training deep learning models and neural networks.

Complete AI Stack

Master TensorFlow, PyTorch, Keras, OpenCV, NLTK, Hugging Face, and LangChain frameworks.

Computer Vision

Build image classifiers, object detection systems, facial recognition, and video analytics.

NLP & ChatGPT

Master natural language processing, transformers, BERT, GPT, and build conversational AI.

Career Support

Placement assistance for AI/ML engineer, data scientist, and AI researcher roles.

Detailed Course Curriculum

Comprehensive topic-wise breakdown of the entire AI & Machine Learning training

1 AI Fundamentals & Python for AI

  • Introduction to Artificial Intelligence
  • History of AI & Current Applications
  • Python for AI (NumPy, Pandas, Matplotlib)
  • Data Manipulation & Visualization
  • Mathematics for AI (Linear Algebra, Calculus)
  • Statistics & Probability Theory
  • Jupyter Notebooks & Google Colab
  • AI Development Environment Setup

2 Machine Learning Fundamentals

  • Introduction to Machine Learning
  • Supervised vs Unsupervised Learning
  • Linear Regression & Polynomial Regression
  • Logistic Regression & Classification
  • Decision Trees & Random Forests
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Naive Bayes & Ensemble Methods

3 Data Preprocessing & Feature Engineering

  • Data Collection & Loading
  • Exploratory Data Analysis (EDA)
  • Handling Missing Data & Outliers
  • Feature Scaling & Normalization
  • Feature Encoding (One-Hot, Label Encoding)
  • Dimensionality Reduction (PCA, t-SNE)
  • Train-Test Split & Cross-Validation
  • Model Evaluation Metrics

4 Deep Learning & Neural Networks

  • Introduction to Deep Learning
  • Artificial Neural Networks (ANN)
  • Activation Functions (ReLU, Sigmoid, Tanh)
  • Backpropagation & Gradient Descent
  • Optimizers (Adam, SGD, RMSprop)
  • Regularization (Dropout, L1/L2)
  • TensorFlow & Keras Framework
  • PyTorch Framework

5 Computer Vision & CNNs

  • Introduction to Computer Vision
  • Image Processing with OpenCV
  • Convolutional Neural Networks (CNN)
  • Image Classification & Recognition
  • Object Detection (YOLO, R-CNN)
  • Facial Recognition & Emotion Detection
  • Image Segmentation & Style Transfer
  • Pre-trained Models (ResNet, VGG, Inception)

6 Natural Language Processing (NLP)

  • Introduction to NLP
  • Text Preprocessing & Tokenization
  • Word Embeddings (Word2Vec, GloVe)
  • Sentiment Analysis & Text Classification
  • Recurrent Neural Networks (RNN, LSTM, GRU)
  • Transformers & Attention Mechanism
  • BERT, GPT, & Large Language Models
  • Chatbot Development & Conversational AI

7 Advanced AI & Generative Models

  • Generative Adversarial Networks (GANs)
  • Variational Autoencoders (VAE)
  • Diffusion Models (Stable Diffusion)
  • Reinforcement Learning (Q-Learning, DQN)
  • Transfer Learning & Fine-tuning
  • Prompt Engineering for LLMs
  • AI Ethics & Responsible AI
  • AI Model Deployment & MLOps

8 Live AI Projects & Industry Applications

  • Image Classification (Cat vs Dog, MNIST)
  • Sentiment Analysis for Social Media
  • Recommendation System (Netflix-style)
  • Chatbot with NLP (Customer Service)
  • Facial Recognition System
  • Fraud Detection in Banking
  • Stock Price Prediction
  • Final Capstone AI Project

Who Should Enroll?

This course is perfect for anyone wanting to build intelligent AI systems

CSE/IT Students

Engineering students wanting to specialize in AI/ML for high-paying tech jobs and research

Data Science Professionals

Data analysts and scientists wanting to transition to AI/ML engineering roles

Software Developers

Programmers wanting to add AI/ML capabilities to their applications and products

AI Researchers

Aspiring researchers wanting to work on cutting-edge AI research and innovation

Ready to Build Intelligent AI Systems?

Join our comprehensive AI training and become an AI/ML expert