TRAINING SYLLABUS:

Module 1: Introduction to Artificial Intelligence

• Overview of Artificial Intelligence and Its Applications
• Understanding AI Terminologies: Machine Learning, Deep Learning, and Neural Networks
• AI Tools and Libraries: TensorFlow, Keras, PyTorch, and Scikit-learn
• Setting Up the Development Environment for AI Projects

Module 2: Machine Learning Fundamentals

• Introduction to Supervised and Unsupervised Learning
• Understanding Classification, Regression, and Clustering Algorithms
• Feature Engineering and Data Preprocessing Techniques
• Evaluating Models: Accuracy, Precision, Recall, F1 Score, and Cross-Validation

Module 3: Deep Learning Basics

• Introduction to Neural Networks and Their Components
• Understanding Activation Functions and Backpropagation
• Building and Training Deep Neural Networks (DNN)
• Introduction to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)

Module 4: Natural Language Processing (NLP)

• Overview of NLP and Its Applications in AI
• Text Preprocessing Techniques: Tokenization, Lemmatization, and Stemming
• Text Classification and Sentiment Analysis
• Working with Word Embeddings: Word2Vec, GloVe, and BERT

Module 5: Computer Vision with AI

• Introduction to Computer Vision and Image Processing
• Working with OpenCV for Image Processing
• Implementing Object Detection and Image Classification with CNNs
• Image Segmentation and Facial Recognition Techniques

Module 6: Reinforcement Learning

• Understanding the Basics of Reinforcement Learning
• Exploring Markov Decision Processes (MDP)
• Learning Algorithms: Q-Learning and Deep Q Networks (DQN)
• Building AI Agents for Problem Solving and Game Playing

Module 7: AI Model Deployment and Scalability

• Model Deployment Techniques for AI Projects
• Using Cloud Platforms (AWS, Azure, Google Cloud) for AI Model Deployment
• Model Optimization for Production: TensorFlow Lite, ONNX
• Scalable Machine Learning Pipelines and Real-Time AI Systems

Module 8: AI Ethics and Responsible AI

• Understanding the Ethical Implications of AI
• Ensuring Fairness, Accountability, and Transparency in AI Systems
• Bias and Discrimination in AI: Detection and Mitigation
• Implementing Responsible AI Practices in Development

Module 9: AI in Industry and Real-World Applications

• Exploring AI Applications in Healthcare, Finance, and Retail
• Building Intelligent Chatbots and Virtual Assistants
• AI in Robotics and Automation
• AI-Driven Data Analytics and Predictive Modeling

Module 10: AI Model Evaluation and Improvement

• Evaluating AI Models: Accuracy, Precision, Recall, F1 Score, AUC-ROC
• Hyperparameter Tuning and Model Optimization Techniques
• Handling Overfitting and Underfitting in AI Models
• Continuous Learning and Model Updates in Production

Module 11: Building AI Projects Portfolio

• Creating AI Projects for Portfolio Showcase
• Documenting and Presenting AI Projects Effectively
• Optimizing AI Projects for Performance and Scalability
• Publishing and Sharing Projects on GitHub and Kaggle

Training Highlights:

• 100% Practical Training with Real-Time Projects
• Guaranteed In-House Placement Assistance
• Training by Experienced Mentors and Industry Experts
• Daily Assignments, Notes, and Progress Tracking
• Collaborative Team Learning Environment
• Exposure to Latest Tools and Technologies in Full-Stack Development