AI+ Developer
Master AI Development: From Fundamentals to Advanced Tools.
Description
The AI+ Developer™ certification provides a comprehensive learning path into core AI development concepts.
Designed for aspiring developers, this program covers key areas like Python programming, data processing, deep learning, and algorithm optimization.
Participants will gain hands-on experience in Natural Language Processing (NLP), Computer Vision, and Reinforcement Learning, enabling them to solve real-world challenges effectively.
The curriculum includes advanced modules on time series analysis, model explainability, and cloud-based deployment strategies. Upon completion, learners will hold the expertise to tackle complex AI system design and deployment, making them industry-ready.
Prerequisites
- Basic Math Knowledge: High-school-level algebra and statistics are desirable.
- Computer Science Fundamentals: Familiarity with variables, functions, loops, and data structures like lists and dictionaries.
- Programming Skills: A foundational understanding of coding is recommended.
Course Outline
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 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
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
Optional Module: AI Agents for Developers
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents