Course Information
- Duration4 weeks (3 hours per week)
Description
Step into the world of Artificial Intelligence with our comprehensive AI course designed for beginners and intermediate learners alike. Whether you're just starting your journey or looking to deepen your knowledge, this course provides a strong foundation in AI concepts, tools, and practical applications.
You'll explore everything from the fundamentals of AI and Machine Learning to cutting-edge technologies like Deep Learning, Computer Vision, and Natural Language Processing. With a balance of theory and hands-on practice, you'll build real AI projects, explore ethical implications, and gain insight into career opportunities in this fast-evolving field.
Course Outline
1: Introduction
- Course Objectives
- Prerequisites
- Tools and Software Required
2: Core AI Concepts
- Overview of Neural Networks
- Feedforward Networks
- Activation Functions (ReLU, Sigmoid, etc.)
- Training and Optimization
- Backpropagation
- Gradient Descent Variants
- Regularization Techniques
- Dropout
- L1/L2 Regularization
3: Applied Machine Learning
- Data Preprocessing
- Handling Missing Data
- Feature Scaling and Encoding
- Model Building and Evaluation
- Cross-Validation
- Metrics (Precision, Recall, F1 Score, etc.)
- Advanced Algorithms
- Ensemble Methods (Random Forest, XGBoost)
- Dimensionality Reduction (PCA, t-SNE)
4: Computer Vision
- Image Processing Basics
- Filters and Edge Detection
- Image Augmentation Techniques
- Convolutional Neural Networks (CNNs)
- Architecture Overview (VGG, ResNet)
- Advanced Topics
- Object Detection (YOLO, SSD)
- Image Segmentation (U-Net, Mask R-CNN)
5: Natural Language Processing (NLP)
- Text Preprocessing
- Tokenization, Stopword Removal
- Lemmatization and Stemming
- Embedding Techniques
- Word2Vec, GloVe, FastText
- Transformer Models
- Applications
- Sentiment Analysis
- Chatbots and Question Answering
6: Reinforcement Learning
- Foundations of Reinforcement Learning
- Markov Decision Processes (MDP)
- Policy and Value Functions
- Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- Policy Gradient Methods
7: AI in Production
- Model Deployment
- Flask/FastAPI Basics
- Model Serving with TensorFlow Serving
- Monitoring and Optimization
- Performance Metrics
- Scalability and Versioning
- Edge AI
- TensorFlow Lite, ONNX Runtime
8: Ethics and Best Practices
- Bias and Fairness in AI
- Explainable AI (XAI)
- Security and Privacy Concerns
9: Projects and Hands-On Practice
- Image Classification Project
- NLP Sentiment Analysis
- Real-Time Object Detection
- Deploying an AI Model
10: Resources
- Books, Articles, and Research Papers
- Online Courses and Tutorials
- Community and Forums
11: Final Assessment and Certification
- Capstone Project
- Multiple-Choice and Coding Exams
- Certification Guidelines