AI+ Developer™ 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

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 Tools you will explore

* GitHub Copilot
* Lobe
* H2O.ai
* Snorkel

Exam: 50 questions, 70% passing, 90 minutes, online proctored exam

Instructor-led OR Self-paced course + Official exam + Digital badge