AIDEVAHAI CertsAH-AIDEV1.0<ul> <li><strong>Python Programming Proficiency</strong><ul> <li>Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence</li></ul></li><li><strong>Deep Learning Techniques</strong><ul> <li>Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges.</li></ul></li><li><strong>Cloud Computing in AI Development</strong><ul> <li>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.</li></ul></li><li><strong>Project Management in AI</strong><ul> <li>Participations will master the skills necessary to manage AI projects effectively, from initiation to completion, including planning, resource allocation, risk management, and stakeholder communication.</li></ul></li></ul><ul> <li>Basic Math: Familiarity with high school-level algebra and basic statistics is desirable.</li><li>Computer Science Fundamentals: Understanding the basic programming concepts (variables, functions, and loops) and data structures (lists and dictionaries).</li><li>Fundamental knowledge of programming skills.</li></ul><h4>Module 1: Foundations of Artificial Intelligence</h4><ul> <li>1.1 Introduction to AI</li><li>1.2 Types of Artificial Intelligence</li><li>1.3 Branches of Artificial Intelligence</li><li>1.4 Applications and Business Use Cases</li></ul><h4>Module 2: Mathematical Concepts for AI</h4><ul> <li>2.1 Linear Algebra</li><li>2.2 Calculus</li><li>2.3 Probability and Statistics</li><li>2.4 Discrete Mathematics</li></ul><h4>Module 3: Python for Developer</h4><ul> <li>3.1 Python Fundamentals</li><li>3.2 Python Libraries</li></ul><h4>Module 4: Mastering Machine Learning</h4><ul> <li>4.1 Introduction to Machine Learning</li><li>4.2 Supervised Machine Learning Algorithms</li><li>4.3 Unsupervised Machine Learning Algorithms</li><li>4.4 Model Evaluation and Selection</li></ul><h4>Module 5: Deep Learning</h4><ul> <li>5.1 Neural Networks</li><li>5.2 Convolutional Neural Networks (CNNs)</li><li>5.3 Recurrent Neural Networks (RNNs)</li></ul><h4>Module 6: Computer Vision</h4><ul> <li>6.1 Image Processing Basics</li><li>6.2 Object Detection</li><li>6.3 Image Segmentation</li><li>6.4 Generative Adversarial Networks (GANs)</li></ul><h4>Module 7: Natural Language Processing</h4><ul> <li>7.1 Text Preprocessing and Representation</li><li>7.2 Text Classification</li><li>7.3 Named Entity Recognition (NER)</li><li>7.4 Question Answering (QA)</li></ul><h4>Module 8: Reinforcement Learning</h4><ul> <li>8.1 Introduction to Reinforcement Learning</li><li>8.2 Q-Learning and Deep Q-Networks (DQNs)</li><li>8.3 Policy Gradient Methods</li></ul><h4>Module 9: Cloud Computing in AI Development</h4><ul> <li>9.1 Cloud Computing for AI</li><li>9.2 Cloud-Based Machine Learning Services</li></ul><h4>Module 10: Large Language Models</h4><ul> <li>10.1 Understanding LLMs</li><li>10.2 Text Generation and Translation</li><li>10.3 Question Answering and Knowledge Extraction</li></ul><h4>Module 11: Cutting-Edge AI Research</h4><ul> <li>11.1 Neuro-Symbolic AI</li><li>11.2 Explainable AI (XAI)</li><li>11.3 Federated Learning</li><li>11.4 Meta-Learning and Few-Shot Learning</li></ul><h4>Module 12: AI Communication and Documentation</h4><ul> <li>12.1 Communicating AI Projects</li><li>12.2 Documenting AI Systems</li><li>12.3 Ethical Considerations</li></ul>- Python Programming Proficiency - Students will gain a solid foundation in Python programming. Implementing AI algorithms, processing data, and constructing AI applications require this competence - Deep Learning Techniques - Learners will master machine learning and deep learning techniques and methods to classification, regression, image recognition, and natural language processing challenges. - 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.- Basic Math: Familiarity with high school-level algebra and basic statistics is desirable. - Computer Science Fundamentals: Understanding the basic programming concepts (variables, functions, and loops) and data structures (lists and dictionaries). - Fundamental knowledge of programming skills.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 and 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 - 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 Considerations5 jours3450.003450.003450.003995.005515.003450.003450.00