Course Overview
AI+ Developer™ certification program offers a tailored journey in key AI domains for developers. Master Python, advanced concepts, math, stats, optimization, and deep learning. The curriculum covers data processing, exploratory analysis, and allows specialization in NLP, computer vision, or reinforcement learning. The program includes time series analysis, model explainability, and deployment intricacies. Upon completion, you'll receive a certification, showcasing your AI proficiency for real-world challenges.
Prerequisites
- Basic Math: Familiarity with high school-level algebra and basic statistics is desirable.
- Computer Science Fundamentals: Understanding the basic programming concepts (variables, functions, and loops) and data structures (lists and dictionaries).
- Fundamental knowledge of programming skills.
Course Objectives
- Python Programming Proficiency
- Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence
- Deep Learning Techniques
- Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges.
- Cloud Computing in AI Development
- Students will get hands-on experience in cloud-based AI application development and learn how to use AWS, Azure, and Google Cloud for scalable AI systems.
- Project Management in AI
- Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.
Outline: AI+ Developer (AIDEV)
Module 1: Foundations of Artificial Intelligence
- 1.1 Introduction to AI
- 1.2 Types of Artificial Intelligence
- 1.3 Branches of Artificial Intelligence
- 1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
- 2.1 Linear Algebra
- 2.2 Calculus
- 2.3 Probability and Statistics
- 2.4 Discrete Mathematics
Module 3: Python for Developer
- 3.1 Python Fundamentals
- 3.2 Python Libraries
Module 4: Mastering Machine Learning
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
Module 5: Deep Learning
- 5.1 Neural Networks
- 5.2 Convolutional Neural Networks (CNNs)
- 5.3 Recurrent Neural Networks (RNNs)
Module 6: Computer Vision
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
Module 7: Natural Language Processing
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
Module 8: Reinforcement Learning
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
Module 9: Cloud Computing in AI Development
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
Module 10: Large Language Models
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
Module 11: Cutting-Edge AI Research
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
Module 12: AI Communication and Documentation
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations