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AI+ Developer™

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Certificate Code: AT-310

Passing Score: 70% (35/50)

Exam Info: 50 MCQs, 90 Minutes

Tagline: Get hands-on with the tools and technologies that power the AI ecosystem.

Course Overview:

  • 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.

Tools Used:

  • GitHub Copilot GitHub Copilot
  • Lobe Lobe
  • H2O.ai H2O.ai
  • Snorkel Snorkel

Modules:

  • Course Overview
    1. Course IntroductionPreview
  • Module 1: Foundations of Artificial Intelligence
    1. 1.1 Introduction to AI Preview
    2. 1.2 Types of Artificial Intelligence Preview
    3. 1.3 Branches of Artificial Intelligence
    4. 1.4 Applications and Business Use Cases
  • Module 2: Mathematical Concepts for AI
    1. 2.1 Linear Algebra Preview
    2. 2.2 Calculus Preview
    3. 2.3 Probability and Statistics Preview
    4. 2.4 Discrete Mathematics
  • Module 3: Python for Developer
    1. 3.1 Python Fundamentals Preview
    2. 3.2 Python Libraries
  • Module 4: Mastering Machine Learning
    1. 4.1 Introduction to Machine Learning
    2. 4.2 Supervised Machine Learning Algorithms
    3. 4.3 Unsupervised Machine Learning Algorithms
    4. 4.4 Model Evaluation and Selection
  • Module 5: Deep Learning
    1. 5.1 Neural Networks
    2. 5.2 Improving Model Performance
    3. 5.3 Hands-on: Evaluating and Optimizing AI Models
  • Module 6: Computer Vision
    1. 6.1 Image Processing Basics
    2. 6.2 Object Detection
    3. 6.3 Image Segmentation
    4. 6.4 Generative Adversarial Networks (GANs)
  • Module 7: Natural Language Processing
    1. 7.1 Text Preprocessing and Representation
    2. 7.2 Text Classification
    3. 7.3 Named Entity Recognition (NER)
    4. 7.4 Question Answering (QA)
  • Module 8: Reinforcement Learning
    1. 8.1 Introduction to Reinforcement Learning
    2. 8.2 Q-Learning and Deep Q-Networks (DQNs)
    3. 8.3 Policy Gradient Methods
  • Module 9: Cloud Computing in AI Development
    1. 9.1 Cloud Computing for AI
    2. 9.2 Cloud-Based Machine Learning Services
  • Module 10: Large Language Models
    1. 10.1 Understanding LLMs
    2. 10.2 Text Generation and Translation
    3. 10.3 Question Answering and Knowledge Extraction
  • Module 11: Cutting-Edge AI Research
    1. 11.1 Neuro-Symbolic AI
    2. 11.2 Explainable AI (XAI)
    3. 11.3 Federated Learning
    4. 11.4 Meta-Learning and Few-Shot Learning
  • Module 12: AI Communication and Documentation
    1. 12.1 Communicating AI Projects
    2. 12.2 Documenting AI Systems
    3. 12.3 Ethical Considerations
  • Optional Module: AI Agents for Developers
    1. 1. Understanding AI Agents
    2. 2. Case Studies
    3. 3. Hands-On Practice with AI Agents

What You’ll Learn:

  • 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.

Career Opportunities:

  • 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.

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%

Self-Study Materials:

  • Videos Videos: Engaging visual content to enhance understanding and learning experience.
  • Podcasts Podcasts: Insightful audio sessions featuring expert discussions and real-world cases.
  • Audiobooks Audiobooks: Listen and learn anytime with convenient audio-based knowledge sharing.
  • E-Books E-Books: Comprehensive digital guides offering in-depth knowledge and learning support.
  • Labs Labs: Interactive lab sessions to apply concepts and strengthen technical skills.
  • Module Wise Quizzes Module Wise Quizzes: Interactive assessments to reinforce learning and test conceptual clarity.
  • Additional Resources Additional Resources: Supplementary references and list of tools to deepen knowledge and practical application.

Frequently Asked Questions:

  • Q: What will I gain from completing this certification?
    A: 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.
  • Q: Do I need any prior AI knowledge to join this course?
    A: 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.
  • Q: Are there any hands-on projects in the course?
    A: 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.
  • Q: Can I choose a specialization during the course?
    A: 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.
  • Q: How will my progress be evaluated?
    A: 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.

Certificate Features:

  • Feature Icon High-Quality Video, E-book & Audiobook
  • Feature Icon Modules Quizzes
  • Feature Icon AI Mentor
  • Feature Icon Access for Tablet & Phone
  • Feature Icon Online Proctored Exam with One Free Retake
  • Feature Icon LABs Practices