Module 1: Introduction to AI
1.1 What is Artificial Intelligence?
1.2 Brief History and Evolution of AI
1.3 Type of AI
1.4 AI Applications and Impact on Various Industries (Healthcare , BFSI, Law ,Retail ,Advertising & Media, Automotive & Transportation, Regional Insights, Market scope Insights)
1.5 Ethical and Social Implications of AI
1.6 AI Tools
Module 2: AI In Cloud
1.7 What is cloud computing?
1.8 Deployment model Vs Service model
1.9 Introduction to Azure , AWS and GCP
1.10 AI Tools in Cloud
Activity – Familiarization of cloud AI tools
Module 3: Machine Learning Fundamentals
3.1 Introduction to Machine Learning
3.2 Framing an ML problem.
3.3 Data Preprocessing and Feature Engineering
3.4 Model Selection, Training, and Evaluation
3.5 Types of Machine Learning: Supervised, Unsupervised, Reinforcement
3.3.1 Regression
3.3.2 Classification
3.3.3 Clustering
3.6 Overfitting, Underfitting, and Regularization
3.7 Evaluation Metrics in Machine Learning
Activity - Create a Regression Model
Activity - Create a Classification Model
Activity - Create a Clustering Model
Module 4: Neural Networks and Deep Learning
4.1 Basics of Neural Networks
4.2 Activation Functions and Network Architectures
4.3 Training Deep Neural Networks
4.4 Convolutional Neural Networks (CNNs)
4.5 Recurrent Neural Networks (RNNs)
Activity -Image recognition
Module 5: Natural Language Processing (NLP)
5.1 Introduction to NLP
5.2 Text Preprocessing and Tokenization
5.3 Sentiment Analysis and Text Classification
5.4 Machine Translation
5.5 NLP Applications
Activity – Create chatbot using no-code or low code solution
Module 6: Computer Vision
6.1 What is Computer Vision?
6.2 How does Computer Vision Work?
6.3 The evolution of computer vision
6.4 Applications of computer vision
6.5 Challenges of computer vision
Activity – Create Computer vision application using no-code or low code solution
Module 7: Future of AI and Emerging Trends
7.1 Current State of AI Research and Development
7.2 Open Challenges and Opportunities in AI
7.3 Generative AI Vs Open AI