Course Description

This comprehensive course will equip you with the knowledge and skills to harness the power of artificial intelligence (AI) and machine learning (ML). You’ll learn about key concepts, techniques, and tools used in building intelligent systems.

Learning Outcomes:

Upon completion of this course, you will be able to:

• Understand the fundamentals of AI and ML.
• Apply machine learning algorithms to solve real-world problems.
• Work with popular AI and ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Develop and deploy AI models for various applications.
• Evaluate the performance of AI models.
• Address ethical considerations in AI development.

Module 1: Introduction to AI and ML

• What is AI and ML?
• Key concepts (supervised vs. unsupervised learning, reinforcement learning)
• Applications of AI and ML
• Ethical considerations in AI

Module 2: Machine Learning Fundamentals

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Support vector machines
• Neural networks
• Clustering algorithms  
• Dimensionality reduction

Module 3: Deep Learning

• Introduction to deep learning
• Convolutional neural networks (CNNs)
• Recurrent neural networks (RNNs)
• Generative adversarial networks (GANs)
• Transfer learning  

Module 4: Natural Language Processing (NLP)

• Text preprocessing
• Word embeddings
• Sentiment analysis
• Machine translation
• Chatbots

Module 5: Computer Vision

• Image classification
• Object detection
• Image segmentation
• Generative models for images

Module 6: Reinforcement Learning

• Markov decision processes
• Q-learning
• Policy gradients
• Deep reinforcement learning

Module 7: AI Tools and Frameworks

• Python libraries (TensorFlow, PyTorch, Scikit-learn)
• Cloud-based AI platforms (Google Cloud AI, AWS SageMaker, Azure Machine Learning)
• Data visualization tools (Matplotlib, Seaborn)

Module 8: AI Applications

• Recommendation systems
• Fraud detection
• Predictive analytics
• Autonomous systems
• Natural language interfaces

Course Projects:

• Build a machine learning model for a real-world problem (e.g., image classification, sentiment analysis)
• Develop a chatbot or virtual assistant
• Create a recommendation system