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Artificial Intelligence

Artificial Intelligence

Home Courses Artificial Intelligence

Artificial Intelligence

Artificial Intelligence training covers the full spectrum of AI technologies, from natural language processing to computer vision. Learners gain hands-on experience in building AI-powered systems that can think, learn, and adapt like humans.

Course Curriculum

Natural Language Processing (NLP)

Basics of NLP:

  • Text Preprocessing (Tokenization, Lemmatization, Stop Words, etc.)
  • Word Embeddings (Word2Vec, GloVe, FastText)
  • Bag of Words, TF-IDF
Advanced NLP Techniques
  • Sequence Models: RNNs, LSTMs, GRUs
  • Attention Mechanism and Transformers
  • BERT, GPT, and other Pretrained Models
  • Named Entity Recognition (NER), Part-of-Speech Tagging
NLP Tasks
  • Sentiment Analysis, Text Classification, Machine Translation
  • Question Answering Systems, Summarization
Key Concepts
  • Vector Representation of Text
  • Sequence Modeling and Language Modeling
  • Self-Attention and Multi-Head Attention Mechanisms
Hands-On
  • Implementation of Text Classification using RNN/LSTM
  • Fine-tuning a Pretrained Transformer Model (BERT or GPT)
  • Building a Chatbot using NLP Techniques
Projects
  • Sentiment Analysis on Social Media Data
  • Text Summarization for News Articles
  • Machine Translation System (English to French)

Computer Vision (CV)

Basics of Computer Vision
  • Image Representation and Preprocessing (Resizing, Normalization, etc.)
  • Convolutional Neural Networks (CNNs)
  • Pooling Layers, Activation Functions
Advanced Computer Vision Techniques
  • Transfer Learning with Pretrained Models (ResNet, VGG, Inception)
  • Object Detection (YOLO, SSD, Faster R-CNN)
  • Image Segmentation (UNet, Mask R-CNN)
  • Generative Models: GANs, Variational Autoencoders (VAEs)
CV Applications
  • Image Classification, Object Detection, Image Segmentation
  • Face Recognition, OCR, Image Style Transfer
Key Concepts
  • Convolutional Filters and Feature Maps
  • Transfer Learning and Fine-tuning
  • Generative Adversarial Networks (GANs) and Their Applications
Hands-On
  • Building and Training a CNN for Image Classification
  • Object Detection using YOLO or Faster R-CNN
  • Image Segmentation using UNet
Projects
  • Face Mask Detection in Real-Time Video Streams
  • OCR for Handwritten Digit Recognition
  • Style Transfer between Images

Large Language Models (LLMs)

Introduction to Large Language Models
  • Overview of LLMs (GPT, BERT, T5)
  • Pretraining and Fine-tuning
  • Transfer Learning in LLMs
Advanced LLM Architectures
  • GPT Family (GPT-2, GPT-3, GPT-4)
  • T5, BART, and Text Generation Models
  • In-context Learning and Prompt Engineering
Applications of LLMs
  • Text Generation and Completion
  • Conversational AI (Chatbots, Virtual Assistants)
  • Zero-shot and Few-shot Learning
  • LLM for Creative Writing and Content Generation
Key Concepts
  • Self-Attention and Transformer Architectures
  • Scaling and Pretraining Large Models
  • Prompt Design and Optimization
Hands-On
  • Fine-tuning GPT for Custom Text Generation
  • Implementing a Conversational AI using GPT or BERT
  • Training LLMs for Summarization Tasks
Projects
  • Building a Virtual Assistant using GPT-4
  • Generating Creative Stories using LLMs
  • Summarizing Long Documents using BART or T5

MLOps for NLP, CV, and LLMs

MLOps Fundamentals
  • Data Pipelines and Model Deployment
  • Monitoring and Maintaining AI Models in Production
  • Version Control and Model Management (MLFlow, DVC)
AI in Production
  • Deployment Strategies (Batch vs. Real-Time Inference)
  • Scalable AI Systems in Cloud (AWS, GCP, Azure)
  • Model Retraining and Continuous Integration/Continuous Deployment (CI/CD) for AI
Special Considerations for NLP and CV Models
  • Managing Drift in NLP and CV Models
  • Optimization and Latency Reduction in Real-Time Systems
Key Concepts
  • Best Practices for Model Deployment and Maintenance
  • CI/CD Pipelines for AI Systems
  • Scaling AI Systems in Cloud Environments
Hands-On
  • Deploying NLP Models as Web Services using Flask/FastAPI
  • Implementing a CI/CD Pipeline for CV Models
  • Monitoring and Retraining LLMs in Production
Projects
  • Deploying a Sentiment Analysis API on Cloud
  • Real-Time Object Detection in a Video Stream with Cloud Deployment
  • Continuous Retraining Pipeline for a Chatbot

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