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

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

1. 12.1 Communicating AI Projects
2. 12.2 Documenting AI Systems
3. 12.3 Ethical Considerations Optional Module: AI Agents for Developers
   • 1. Understanding AI Agents
   • 2. Case Studies
   • 3. Hands-On Practice with AI Agents
   Tools you will explore
   • GitHub Copilot
   • Lobe
   • H2O.ai
   • Snorkel

Exam: 50 questions, 70% passing, 90 minutes, online proctored examInstructor-led OR Self-paced course + Official exam + Digital badge