• Basics of Python programming for AI development
• Data structures, loops, and conditional statements
• Working with Numpy, Pandas, and Matplotlib
• Data cleaning, preprocessing, and visualization
• File handling and data manipulation using Python
• Implementing mini projects using Python for AI tasks

• Understanding Machine Learning and its types
• Supervised, Unsupervised, and Reinforcement Learning
• Regression and Classification algorithms
• Model training, testing, and evaluation metrics
• Feature selection and data normalization
• Implementing ML models using Scikit-learn

• Introduction to Artificial Neural Networks (ANNs)
• Understanding activation functions and backpropagation
• Convolutional Neural Networks (CNNs) for image recognition
• Recurrent Neural Networks (RNNs) and LSTMs for sequential data
• Hands-on model building using TensorFlow and Keras
• Hyperparameter tuning and model optimization

• Text preprocessing – tokenization, stemming, and lemmatization
• Understanding bag-of-words and TF-IDF techniques
• Sentiment analysis and text classification models
• Chatbot and voice assistant basics
• Named Entity Recognition (NER) and topic modeling
• Introduction to Transformer-based models like BERT and GPT

• Working with TensorFlow, PyTorch, and OpenCV
• Model saving, exporting, and version control
• Integrating AI with cloud platforms (AWS, Azure, GCP)
• Model deployment using Flask or FastAPI
• Monitoring AI models in production
• Introduction to MLOps and automation tools

• End-to-end real-time AI project development
• Problem definition, data collection, and model building
• Model evaluation and fine-tuning
• Project presentation and review by mentors
• Career guidance, resume preparation, and mock interviews
• Earn your AI Certification after successful completion