AI+ Developer™
AI+ Developer™
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
Prerequisites
- Basic math, including familiarity with high school-level algebra and basic statistics, is desirable.
- Understanding basic programming concepts such as variables, functions, loops, and data structures like lists and dictionaries is essential.
- A fundamental knowledge of programming skills is required.
- Course IntroductionPreview
- 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
Passing Score : 70% (35/50)
Number of Multiple Choice Question : 50 MCQs, 90 Minutes
Number of exams required for certification : 1
Exam Blueprint
- Foundations of Artificial Intelligence (AI) – 5%
- Mathematical Concepts for AI – 5%
- Python for AI Development – 10%
- Mastering Machine Learning – 15%
- Deep Learning – 10%
- Computer Vision – 10%
- Natural Language Processing (NLP) – 15%
- Reinforcement Learning – 5%
- Cloud Computing in AI Development – 10%
- Large Language Models (LLMs) – 5%
- Cutting-Edge AI Research – 5%
- AI Communication and Documentation – 5%
Python Programming Proficiency
Students will gain a solid foundation in Python programming, a crucial skill for implementing AI algorithms, processing data, and building AI applications effectively.
Deep Learning Techniques
Learners will master machine learning and deep learning techniques to address challenges in classification, regression, image recognition, and natural language processing.
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.
AI Machine Learning Developer
Design, implement, and optimize algorithms and models to enable systems to learn from data and make predictions or decisions.
AI Solutions Architect
Design and implement AI systems that integrate seamlessly with existing infrastructure to address business needs effectively and enhance system capabilities.
AI Application Developer
Build, design, and maintain AI-driven applications that solve real-world problems, integrating AI technologies for enhanced functionality.
AI System Programmers
Develop and maintain AI systems, including programming algorithms and software components that enable intelligent behavior in machines and applications.
FAQ
What will I gain from completing this certification?
Upon completion, you will receive an AI+ Developer™ certification, showcasing your proficiency in AI. You’ll have the skills to tackle real-world AI challenges and implement advanced AI solutions in various domains.
Do I need any prior AI knowledge to join this course?
While prior AI knowledge is not mandatory, a fundamental understanding of Python programming and basic math and statistics will help you grasp the advanced concepts covered in this course.
Are there any hands-on projects in the course?
Yes, the course includes various hands-on projects and practical exercises to help you apply theoretical concepts to real-world scenarios, reinforcing your learning through practical experience.
Can I choose a specialization during the course?
You cannot choose a specialization in this course. However, you will be trained in areas such as Natural Language Processing (NLP), computer vision, and reinforcement learning.
How will my progress be evaluated?
Your progress will be evaluated through a combination of quizzes, hands-on exercises, and a final assessment. These evaluations are designed to test your understanding and application of the material.
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