Full course content

• • Class 1 - Introduction to AI and Machine Learning
• • Class 2 - Building a classification model with supervised learning
• • Class 3 - Introduction to unsupervised learning Building an unsupervised learning model
• • Class 4 - Understanding Convolutional Neural Networks (CNNs)
• • Class 5 - Introduction to Natural Language Processing (NLP)
• • Class 6 - Building a simple Reinforcement Learning model & its techniques
• • Class 7 - Introduction to GANs (Generative Adversarial Networks) Building a GAN project
• • Class 8 - Introduction to Transfer Learning Building a Transfer Learning project
• • Class 9 -Understanding Data Pre-processing & Feature Engineering
• • Class 10 - Introduction to Hyperparameter Tuning Building a hyperparameter tuning pipeline
• • Class 11 - Introduction to Cloud Computing & Deployment Deploying your AI model to the cloud
• • Class 12 - Introduction to Natural Language Processing (NLP) applications Building a chatbot
• • Class 13 - Introduction to Speech Recognition & Synthesis Building a speech recognition
• • Class 14 - Introduction to Time Series Analysis Building a time series forecasting model
• • Class 15 - Introduction to Recommender Systems Building a recommender system
• • Class 16 - Introduction to AI in Business Building a fraud detection mode
• • Class 17 - Introduction to AI in Education Building an AI-powered educational tool
• • Class 18 - Introduction to Explainable AI (XAI) Building an XAI model
• • Class 19 - Introduction to AI Ethics and Bias Understanding the importance of ethical
• • Class 20 - Conclusion and Recap Q&A session
• • Class 21
• • Class 22