Artificial Intelligence (AI)

Module 1: Introduction to Artificial Intelligence

• AI history, applications, and ethics
• Overview of AI subfields

Module 2: Mathematics for AI

• Linear algebra, probability, and statistics
• Calculus basics for optimization

Module 3: Programming Foundations Python programming essentials for AI Libraries: NumPy, Pandas, Matplotlib

Module 4: Machine Learning Basics

• Types: supervised, unsupervised, reinforcement
• Algorithms: linear regression, decision trees, clustering

Module 5: Deep Learning

• Neural networks architecture
• CNNs for image recognition
• RNNs and LSTMs for sequence modeling

Module 6: Natural Language Processing (NLP)

• Text preprocessing and tokenization
• Sentiment analysis
• Transformers and language models (e.g., BERT, GPT)

Module 7: Computer Vision

• Image classification
• Object detection and segmentation

Module 8: AI Model Deployment & MLOps

• Model evaluation and tuning
• Deploying AI models on cloud platforms
• Monitoring and maintaining AI systems

Module 9: Capstone Project

• End-to-end AI solution development
• Real-world problem solving with AI

Experience in the Industry Gain knowledge from experts with practical Artificial Intelligence (AI) project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Accenture, and Cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques, and best practices.