AI+ Developer Practitioner™
- From 505 EUR + VAT
- Self-paced online Start anytime
- Duration 40 hours of content, self-paced
- Language English
- Prerequisite Basic math, computer science fundamentals, fundamental programming skills
- Topic AI Development
Overview
What the course covers
The course, the materials and the exam are in English. The description below is the original AI CERTs® text.
- Core AI Foundations: Covers Python, deep learning, data processing, and algorithm design
- Hands-on Projects: Focus on NLP, computer vision, and reinforcement learning
- Advanced Modules: Includes time series, model explainability, and cloud deployment
- Industry-Ready Skills: Prepares learners to design and deploy complex AI systems
Prerequisites
Basic math, computer science fundamentals, fundamental programming skills
Audience
Who is it for
Software Developers: Enhance your coding expertise by mastering AI algorithms and deep learning techniques.
Data Enthusiasts: Apply AI-driven data analysis, machine learning models, and deep learning to solve complex problems.
Computer Vision & NLP Researchers: Dive into specialized AI fields, including computer vision and natural language processing.
IT Specialists & System Architects: Integrate AI solutions into existing systems and optimize performance.
Students & Fresh Graduates: Build a strong foundation in AI development and prepare for future opportunities in tech.
In the price
What the price includes
- Self-paced online course with the full learning material
- The official AI CERTs® exam
- Digital badge after passing the exam
Course content
Syllabus
Course Overview
Course IntroductionPreview
Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI Preview 1.2 Types of Artificial Intelligence Preview 1.3 Branches of Artificial Intelligence 1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
2.1 Linear Algebra Preview 2.2 Calculus Preview 2.3 Probability and Statistics Preview 2.4 Discrete Mathematics
Module 3: Python for Developer
3.1 Python Fundamentals Preview 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
Exam
About the exam
- 50 Questions
- 90 min Time limit
- 70% Pass mark
- Online proctored exam
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