AI+ Developer (AT-310 Self-Paced Training)
Ziele der Schulung
The AI+ Developer certification provides a comprehensive learning path into core AI development concepts, designed for aspiring developers. It equips learners with key skills in Python programming, data processing, deep learning, and algorithm optimization, along with hands-on experience in Natural Language Processing, Computer Vision, and Reinforcement Learning, to solve real-world challenges effectively and become industry-ready in AI system design and deployment. What Will You Learn? Learners will gain practical knowledge of AI foundations, machine learning and deep learning techniques, modern AI frameworks, and the application of AI solutions across real-world use cases.
Zielgruppe Seminar
- Software Developers seeking to master AI algorithms and deep learning
- Data Enthusiasts applying machine learning and deep learning to complex problems
- Computer Vision & NLP Researchers tackling specialized AI fields
- IT Specialists & System Architects integrating AI into existing systems
- Students & Fresh Graduates building a strong foundation in AI development
Voraussetzungen
- Basic math, including high-school-level algebra and basic statistics
- Computer science fundamentals (variables, functions, loops, data structures)
- Fundamental programming skills are recommended
Seminarinhalt
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 Researc
- 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
- Understanding AI Agents
- Case Studies
- Hands-On Practice with AI Agents
Hinweise
Prüfung und Zertifizierung
- Duration: 90 Minutes
- Passing Score: 70%
- Format: 50 Multiple Choice Questions
- Delivery Method: Online proctored exam
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Übersicht: AI Certs Schulungen Portfolio
