Advanced Certification In Artificial Intelligence

Module-1: Introduction to Artificial Intelligence

• Overview of AI, ML, and DL
• History and evolution of AI
• Applications of AI in various industries
• Ethical considerations in AI

Module-2: Python Programming for AI

• Introduction to Python programming language
• Data structures and control flow
• Functions and modules
• Introduction to OOPs
• Working with libraries like NumPy, pandas, and Matplotlib

Standalone certificate: Python Pioneers upon completion of Module 1 & 2

Module-3: Fundamentals of Statistics

• Introduction to Data and Statistics
• Collecting and Organizing Data
• Descriptive Statistics
• Data Visualization
• Probability and Random Variables
• Inferential Statistics
• Correlation and Regression
• Data Cleaning
• Data Exploration

Module-4: Fundamentals of Machine Learning

• Introduction to ML algorithms: supervised, unsupervised, and reinforcement learning
• Model evaluation and selection
• Feature engineering and data preprocessing techniques
• Introduction to scikit-learn library

Module-5: Deep Learning Basics

• Neural network architecture and components
• Training neural networks with backpropagation
• Introduction to TensorFlow or PyTorch framework
• Building and training basic neural network models

Standalone certificate: AI Pathfinder upon completion of Module 1 – 5

Module-6: Advanced Deep Learning

• Convolutional Neural Networks (CNNs) for computer vision tasks
• Recurrent Neural Networks (RNNs) for sequential and time series data
• Transfer learning and fine-tuning pre-trained models
• Introduction to Generative Adversarial Networks (GANs)

Module-7: Natural Language Processing (NLP)

• Text preprocessing and tokenization
• Word embeddings and text vectorization
• NLP tasks: sentiment analysis, named entity recognition, text classification,token classification, question answering, summarization, sentence similarity.
• Introduction to NLP libraries like NLTK, spaCy, and Transformers
• Transformer architectures such as BERT and GPT, difference between autoregressive and bidirectional architectures.

Module-8: Computer Vision

• Image preprocessing techniques
• Object detection and localization
• Image classification and segmentation
• Hands-on projects using OpenCV or TensorFlow for computer vision tasks

Standalone certificate: Deep Dive Maestro upon completion of Module 1 – 8

Module-9: Cloud Computing for AI

• Introduction to cloud platforms (e.g., AWS, Azure, Google Cloud)
• Cloud-based storage and computing services
• Deploying and scaling AI models on the cloud
• Introduction to AWS SageMaker or similar services

Module-10: Capstone Project

• Real-world project where students apply their knowledge and skills
• Project planning, execution, and documentation
• Presentation of project findings and outcomes