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AI+ Developer™ Instructor Guide
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Introduction
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Module 1: Foundations of Artificial Intelligence
1.1 Introduction to AI
1.2 Types of Artificial Intelligence
1.3 Branches of Artificial Intelligence
1.4 Applications and Business Use Cases
Module 2: Mathematical Concepts for AI
2.1 Linear Algebra
2.2 Calculus
2.3 Probability & Statistics
2.4 Discrete Mathematics
Module 3: Python for Developer
3.1 Python fundamentals
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 Convolutional Neural Networks (CNNs)
5.3 Recurrent Neural Networks (RNNs)
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
Module 12: AI Communication and Documentation
12.1 Communicating AI Projects
12.2 Documenting AI Systems
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AI+ Developer™ Instructor Guide
