AI+ Developer Practitioner™ eLearning
Get hands-on with the tools and technologies that power the AI ecosystem.
- 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
Duur: 5 dag(en)
- 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.
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Master Key AI Development Skills:
Learn Python, deep learning, advanced concepts, and optimization techniques to build robust AI solutions.
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Specialize in Cutting-Edge AI Domains:
Gain expertise in NLP, computer vision, or reinforcement learning, alongside data processing, exploratory analysis, and time series analysis.
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Stay Ahead in AI Development:
AI is transforming industries, and organizations seek developers with strong proficiency in deploying AI models to solve real-world problems.
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Advance Your Career in AI Development:
With growing demand across tech, finance, and healthcare sectors, this certification positions you as a leader in AI-driven development.
Inhoud
- Course IntroductionPreview
- 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
- 2.1 Linear Algebra Preview
- 2.2 Calculus Preview
- 2.3 Probability and Statistics Preview
- 2.4 Discrete Mathematics
- 3.1 Python Fundamentals Preview
- 3.2 Python Libraries
- 4.1 Introduction to Machine Learning
- 4.2 Supervised Machine Learning Algorithms
- 4.3 Unsupervised Machine Learning Algorithms
- 4.4 Model Evaluation and Selection
- 5.1 Neural Networks
- 5.2 Improving Model Performance
- 5.3 Hands-on: Evaluating and Optimizing AI Models
- 6.1 Image Processing Basics
- 6.2 Object Detection
- 6.3 Image Segmentation
- 6.4 Generative Adversarial Networks (GANs)
- 7.1 Text Preprocessing and Representation
- 7.2 Text Classification
- 7.3 Named Entity Recognition (NER)
- 7.4 Question Answering (QA)
- 8.1 Introduction to Reinforcement Learning
- 8.2 Q-Learning and Deep Q-Networks (DQNs)
- 8.3 Policy Gradient Methods
- 9.1 Cloud Computing for AI
- 9.2 Cloud-Based Machine Learning Services
- 10.1 Understanding LLMs
- 10.2 Text Generation and Translation
- 10.3 Question Answering and Knowledge Extraction
- 11.1 Neuro-Symbolic AI
- 11.2 Explainable AI (XAI)
- 11.3 Federated Learning
- 11.4 Meta-Learning and Few-Shot Learning
- 12.1 Communicating AI Projects
- 12.2 Documenting AI Systems
- 12.3 Ethical Considerations
- 1. Understanding AI Agents
- 2. Case Studies
- 3. Hands-On Practice with AI Agents
- GitHub Copilot
- Lobe
- H2O.ai
- Snorkel
Lesmethode
Instructor-led OR Self-paced course + Official exam + Digital badge
Kenmerken
Online proctored exam included, with one free retake.
Exam format:
50 questions, 70% passing, 90 minutes, online proctored exam
Access to all materials and exams is provided for 365 days after delivery.
Voorkennis
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Basic math, computer science fundamentals, fundamental programming skills

