Artificial Intelligence Course

Dive deep into the field of Artificial Intelligence (AI) with our comprehensive course. This course covers everything from the fundamentals of AI to advanced techniques, including machine learning, neural networks, and real-world applications. Gain hands-on experience and develop the skills necessary to tackle complex AI problems.

Course Curriculum:

• Hour 1: Introduction to Artificial Intelligence and Its Applications
• Hour 2: History and Evolution of AI
• Hour 3: Fundamentals of Machine Learning
• Hour 4: Data Preprocessing and Feature Engineering
• Hour 5: Supervised Learning Algorithms (Regression, Classification)
• Hour 6: Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
• Hour 7: Neural Networks and Deep Learning Basics
• Hour 8: Convolutional Neural Networks (CNNs) for Image Recognition
• Hour 9: Recurrent Neural Networks (RNNs) and LSTM for Time Series
• Hour 10: Generative Adversarial Networks (GANs)
• Hour 11: Natural Language Processing (NLP) Fundamentals
• Hour 12: Advanced NLP Techniques (Transformers, BERT)
• Hour 13: Reinforcement Learning Basics
• Hour 14: Model Evaluation and Hyperparameter Tuning
• Hour 15: AI Ethics and Responsible AI
• Hour 16: Introduction to AI Frameworks (TensorFlow, PyTorch)
• Hour 17: Building Models with TensorFlow
• Hour 18: Building Models with PyTorch
• Hour 19: Deployment of AI Models
• Hour 20: AI in Real-World Applications
• Hour 21: AI for Healthcare
• Hour 22: AI for Financial Forecasting
• Hour 23: AI in Robotics
• Hour 24: AI for Game Development
• Hour 25: AI and the Internet of Things (IoT)
• Hour 26: Ethical Considerations and Future Trends in AI
• Hour 27: Practical Challenges in AI Implementation
• Hour 28: Case Studies and Industry Trends
• Hour 29: Career Pathways in AI and Advanced Topics
• Hour 30: Course Review and Final Assessment
• Project Work (10 Hours):
• Hour 31: Project 1: Image Classification with CNNs
• Hour 32: Project 2: Text Classification with NLP
• Hour 33: Project 3: Time Series Forecasting with RNNs
• Hour 34: Project 4: Generative Adversarial Networks (GANs) for Data Augmentation
• Hour 35: Project 5: AI for Healthcare Applications
• Hour 36: Project 6: AI for Financial Forecasting
• Hour 37: Project 7: AI in Robotics
• Hour 38: Project 8: AI for Game Development
• Hour 39: Project 9: Building an AI Chatbot
• Hour 40: Project 10: End-to-End AI Solution