Artificial Intelligence

Course Overview

1. Introduction to Artificial Intelligence (AI)

• What is AI?: Definition, history, and evolution of AI.
• Types of AI: Narrow AI, General AI, and Super AI.
• AI in Industries: Applications and impact of AI across sectors like healthcare, finance, retail, and transportation.
• Ethical AI: Understanding the ethical considerations and challenges in AI deployment.

2. Machine Learning (ML) Foundations

• Introduction to Machine Learning: Differences between AI, ML, and Deep Learning.
• Types of Learning: Supervised, Unsupervised, and Reinforcement Learning.
• Supervised Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).
• Unsupervised Learning Algorithms: Clustering (K-Means, DBSCAN), Dimensionality Reduction (PCA, t-SNE).
• Reinforcement Learning: Basics of Q-Learning and policy gradient methods.

3. Deep Learning (DL) Techniques

• Neural Networks Basics: Perceptron, Multi-Layer Perceptron (MLP), Activation Functions (ReLU, Sigmoid, Tanh).
• Convolutional Neural Networks (CNNs): Layers, pooling, and applications in image recognition.
• Recurrent Neural Networks (RNNs) and LSTMs: Sequence data processing, applications in time series, and language modeling.
• Advanced DL Architectures: Autoencoders, Variational Autoencoders (VAEs), and Transformer models.

4. Introduction to Generative AI

• Generative Models Overview: What are generative models and their significance?
• Applications of Generative AI: Text generation, image synthesis, music composition, and creative content generation.
• Generative AI in Industries: Use cases in gaming, entertainment, healthcare, and more.

5. Generative Adversarial Networks (GANs)

• GAN Basics: Understanding the architecture—generator and discriminator.
• Training GANs: Steps, challenges (mode collapse, instability), and solutions (Wasserstein GANs, Progressive GANs).
• Applications of GANs: Image generation, deepfakes, super-resolution imaging, and style transfer.
• Hands-On with GANs: Implementing basic GANs using TensorFlow and PyTorch.

6. Natural Language Processing (NLP)

• NLP Fundamentals: Tokenization, stemming, lemmatization, and Part-of-Speech (POS) tagging.
• Sentiment Analysis and Named Entity Recognition (NER): Techniques and real-world applications.
• NLP Models: Word Embeddings (Word2Vec, GloVe), Recurrent Neural Networks (RNNs), and Transformers (BERT, GPT).
• Language Generation: Deep dive into GPT models for text generation and conversational AI.

7. AI in Computer Vision

• Image Classification and Object Detection: Techniques using CNNs and YOLO.
• Facial Recognition: Building and deploying facial recognition models.
• AI in Augmented Reality (AR): Integration of AI with AR for immersive experiences.
• Hands-On Projects: Building image classifiers and object detection systems.

8. AI Tools and Frameworks

• TensorFlow and PyTorch: Building and deploying AI models with popular frameworks.
• Keras for Deep Learning: High-level API for quick prototyping and experimentation.
• Scikit-learn: Machine learning tools for data analysis and model building.
• Generative AI Tools: Working with OpenAI’s GPT, DALL·E, and Hugging Face transformers.

9. AI in Real-World Applications

• Healthcare AI: Predictive analytics, diagnostics, and personalized medicine.
• Finance AI: Fraud detection, algorithmic trading, and customer service automation.
• Retail AI: Recommendation systems, inventory management, and customer insights.
• Case Studies: In-depth exploration of AI implementations in various industries.

10. Data Science Integration with AI

• Data Preprocessing: Data cleaning, normalization, and feature engineering.
• Data Visualization: Tools like Matplotlib, Seaborn, and Plotly for effective visualization.
• AI Model Evaluation: Metrics like accuracy, precision, recall, F1 score, ROC-AUC for model performance assessment.

11. AI Ethics, Bias, and Governance

• Ethical AI Development: Addressing bias, fairness, and transparency in AI.
• AI Governance: Frameworks for responsible AI development and deployment.
• AI for Social Good: Leveraging AI to address global challenges such as climate change and public health.

12. Capstone Projects and Real-World Scenarios

• End-to-End AI Projects: Build and deploy AI solutions for NLP, computer vision, and generative AI applications.
• Real-World Applications: Apply AI techniques to solve industry-specific problems in e-commerce, healthcare, and finance.

13. Certification Preparation and Career Pathways

• Exam Preparation: Practice tests, quizzes, and tips for certification exams.
• Certification Opportunities: AWS Certified Machine Learning, Google AI Certification, Microsoft Certified: AI Engineer Associate, and others.
• Career Guidance: Insights into AI job roles, industry demand, and career progression paths.