Course Overview:
This 6-month certificate course is designed to provide a comprehensive introduction to Artificial Intelligence (AI) for beginners. The course will cover foundational concepts, tools, and techniques in AI, including machine learning, neural networks, natural language processing, and ethical considerations. By the end of the course, students will have a solid understanding of AI fundamentals and be able to apply basic AI techniques to real-world problems.
Course Outline
Month 1: Introduction to Artificial Intelligence
- Week 1: What is AI?
- Definition and history of AI
- Types of AI: Narrow AI, General AI, and Superintelligent AI
- Applications of AI in various industries
- Week 2: AI Concepts and Terminology
- Key concepts: Machine Learning, Deep Learning, Neural Networks
- AI vs. Machine Learning vs. Deep Learning
- Overview of AI tools and frameworks (e.g., TensorFlow, PyTorch)
- Week 3: Mathematics for AI
- Linear algebra, calculus, and probability basics
- Importance of mathematics in AI
- Week 4: Programming for AI
- Introduction to Python for AI
- Python libraries for AI (NumPy, Pandas, Matplotlib)
Month 2: Machine Learning Basics
- Week 1: Introduction to Machine Learning (ML)
- What is Machine Learning?
- Types of ML: Supervised, Unsupervised, and Reinforcement Learning
- Week 2: Supervised Learning
- Regression and Classification
- Algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors (KNN)
- Week 3: Unsupervised Learning
- Clustering and Dimensionality Reduction
- Algorithms: K-Means, Principal Component Analysis (PCA)
- Week 4: Model Evaluation and Optimization
- Overfitting and Underfitting
- Cross-validation and Hyperparameter Tuning
Month 3: Deep Learning and Neural Networks
- Week 1: Introduction to Deep Learning
- What is Deep Learning?
- Neural Networks: Structure and Function
- Week 2: Building Neural Networks
- Forward and Backward Propagation
- Activation Functions: ReLU, Sigmoid, Softmax
- Week 3: Convolutional Neural Networks (CNNs)
- Basics of CNNs
- Applications in Image Recognition
- Week 4: Recurrent Neural Networks (RNNs)
- Basics of RNNs
- Applications in Time Series and Natural Language Processing (NLP)
Month 4: Natural Language Processing (NLP)
- Week 1: Introduction to NLP
- What is NLP?
- Applications: Chatbots, Sentiment Analysis, Translation
- Week 2: Text Preprocessing
- Tokenization, Stemming, Lemmatization
- Bag of Words and TF-IDF
- Week 3: NLP Models
- Word Embeddings: Word2Vec, GloVe
- Introduction to Transformers
- Week 4: Building a Simple NLP Project
- Sentiment Analysis or Text Classification
Month 5: AI Ethics and Real-World Applications
- Week 1: AI Ethics
- Bias in AI
- Privacy and Security Concerns
- Ethical AI Development
- Week 2: AI in Industry
- Case Studies: Healthcare, Finance, Retail, Autonomous Vehicles
- Week 3: AI Tools and Platforms
- Overview of AI platforms (Google AI, IBM Watson, Azure AI)
- Cloud-based AI services
- Week 4: Capstone Project Planning
- Brainstorming project ideas
- Defining project scope and objectives
Month 6: Capstone Project and Course Wrap-Up
- Week 1-4: Capstone Project
- Students work on a real-world AI project
- Projects can include:
- Building a simple ML model
- Developing a chatbot
- Creating an image recognition system
- Final Week: Project Presentation and Course Review
- Students present their projects
- Course review and Q&A session
- Certificate distribution
Course Fee
- Total Course Fee: $7,200 (or equivalent in local currency)
Additional Information
- Prerequisites: Basic knowledge of programming (preferably Python) and high school-level mathematics.
- Mode of Delivery: Online (Live sessions + Recorded lectures)
- Tools and Software: Python, Jupyter Notebook, TensorFlow, PyTorch
- Certification: Certificate of Completion issued upon successful completion of the course and capstone project.
This course is ideal for students, professionals, and enthusiasts looking to build a strong foundation in AI and explore its potential in various domains.
