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Udemy - Risk And Ai (Rai) Garp Prep Course - OneDDL - 05-28-2025 Free Download Udemy - Risk And Ai (Rai) Garp Prep Course Published 5/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 6.07 GB | Duration: 18h 59m Master the GARP Risk and AI Certification: Understand AI risks, governance, and applications in finance What you'll learn Understand the foundational concepts of Artificial Intelligence and Machine Learning Analyze and evaluate the risks associated with AI models Apply governance and risk management frameworks Prepare effectively for the GARP Risk and AI Certification exam Requirements No prior experience with AI or risk management is required A basic understanding of finance or risk concepts Familiarity with business or technology terminology used in financial services will enhance the learning experience but is not a strict prerequisite. Description Are you ready to future-proof your career at the intersection of finance, risk management, and artificial intelligence?This course is your ultimate companion to prepare for the GARP Risk and AI (RAI) Certification-the world's first global certification designed to equip professionals with a deep understanding of AI risks, governance, and regulatory expectations in the financial services industry.Whether you're a risk manager trying to keep pace with emerging technologies, a data scientist navigating model governance, a compliance officer concerned with responsible AI use, or student freshly embarked on an AI journey, this course is built for you.Through concise lessons, real-world case studies, practice questions, and exam-oriented guidance, you'll gain:A strong grasp of AI/ML fundamentals tailored for financePractical insights into identifying, measuring, and mitigating AI-related risksFrameworks for ethical AI, model validation, and regulatory complianceA strategic study plan aligned with GARP's official RAI syllabusNo prior technical or AI background? No problem. This course breaks down complex concepts into clear, actionable knowledge.Join now and take a confident step toward becoming a future-ready risk professional with GARP's Risk and AI Certification. This course is taught by professionals working in the AI domain and have thousands of students across more than 100 countries! Overview Section 1: Welcome and Overview Lecture 1 Course Overview Lecture 2 Practice Test Lecture 3 Join the Community for Live Classes and Q&A Sessions Section 2: Module I Lecture 4 Classical AI Lecture 5 Specific Vs General AI Lecture 6 Good Old Fashioned AI (GOFAI) Lecture 7 Simple Reinforcement Learning Lecture 8 Lookahead Lecture 9 Search in AI Lecture 10 Recursion Lecture 11 Recursive Adversarial Tree Search in AI Lecture 12 Complexity, Heuristics, and Reinforcement Learning Lecture 13 Limits of Classical AI Lecture 14 Introducing Neural Nets Lecture 15 Artificial Neuron Lecture 16 Connectionism and Its Early Challenges Lecture 17 Deep Learning Lecture 18 DL Beats Symbolic AI at Its Own Game Lecture 19 Inscrutability of Deep Learning Lecture 20 Dawn of AGI Lecture 21 ML & Risks Lecture 22 Examples of Unsupervised Learning - PCA Lecture 23 Risks of Inscrutability Lecture 24 Risks of Overreliance Lecture 25 Risks to Us Section 3: Module 2 - Chapter 1: Intro to Tools Lecture 26 Introduction Lecture 27 Types of ML Lecture 28 Exploratory Data Analysis Lecture 29 Data Cleaning Lecture 30 Data Visualization Lecture 31 Feature Extraction Lecture 32 Data Scaling Lecture 33 Data Transformation Lecture 34 Dimensionality Reduction Techniques Lecture 35 Training, Validation, and Testing Lecture 36 Software for Machine Learning Section 4: Module 2: Chapter 2 - Unsupervised Learning Lecture 37 Introduction Lecture 38 K-Means Algorithm Lecture 39 Performance Management Lecture 40 Selecting Centroids Lecture 41 Selection of Centroids - Example Lecture 42 Advantages and Problems of K-Means Lecture 43 Fuzzy K-Means Lecture 44 Hierarchical Clustering Lecture 45 Density Based Clustering Section 5: Module 2 - Chapter 3: Simple Linear Regression Lecture 46 Introduction: Simple Linear Regression Lecture 47 Multi Linear Regression Lecture 48 Wage Rates Example Lecture 49 Potential Problems in Regression Lecture 50 Stepwise Regression Procedure Lecture 51 Classification Problem Lecture 52 Other Types of Limited Dependent Variable Models Lecture 53 Linear Discriminant Analysis Section 6: Module 2 - Chapter 4: Supervised Learning - Part II Lecture 54 Introduction Lecture 55 Regression Trees Lecture 56 Classification Trees Lecture 57 Pruning Lecture 58 Ensemble Methods Lecture 59 K-Nearest Neighbors Lecture 60 Support Vector Machines Lecture 61 SVM Example and Extensions Lecture 62 Neural Networks Lecture 63 Choice of Activation Function Lecture 64 Numerical Example Lecture 65 Backpropagation Lecture 66 Architectural Issues Lecture 67 Overfitting Lecture 68 Advanced Neural Network Structures Lecture 69 Autoencoders Section 7: Module 2 - Chapter 5: Semi-Supervised Learning Lecture 70 Introdution Lecture 71 Techniques Lecture 72 Self-Training Lecture 73 Co-Training Lecture 74 Unsupervised Preprocessing Section 8: Module 2: Chapter 6 - Reinforcement Learning Lecture 75 Intro to RL Lecture 76 Multi-Arm Bandit Lecture 77 Strategies in RL Lecture 78 Markov Decision Process Lecture 79 Approaches to RL Lecture 80 The Bellman Equations Section 9: Module 2: Chapter 7 - Supervised Learning - Model Estimation Lecture 81 Ordinary Least Squares Lecture 82 Non Linear Squares Lecture 83 Hill Climbing Lecture 84 The Gradient Descent Method Lecture 85 Backpropagation Lecture 86 Computational Issues Lecture 87 Maximum Likelihood Lecture 88 Overfitting Lecture 89 Underfitting Lecture 90 Bias-variance Trade Off Lecture 91 Prediction Accuracy Versus Interpretability Lecture 92 Regularization - Ridge Regression Lecture 93 LASSO Lecture 94 Elastic Net Lecture 95 Regularization Example Lecture 96 Cross Validation Lecture 97 Stratified Cross-validation Lecture 98 Bootstrapping Lecture 99 Grid Searches Section 10: Module 2: Chapter 8 - Supervised Learning - Model Performance Evaluation Lecture 100 Introduction - Model Evaluation Lecture 101 Model Performance Evaluation - Continuous Variable Lecture 102 Classification Model Prediction Lecture 103 Model Performance Evaluation - Classification Section 11: Module 2: Chapter 9 - NLP Lecture 104 Introduction Lecture 105 Data Preprocessing Lecture 106 NLP Models Lecture 107 Vector Normalization Lecture 108 Dictionary Comparison Approaches Lecture 109 N Grams Lecture 110 TF-IDF Lecture 111 ML Approaches Lecture 112 Naive Bayes Lecture 113 Word Meaning Lecture 114 NLP Evaluation Section 12: Module 2: Chapter 10 - Generative AI Lecture 115 Intro - GenAI Lecture 116 Intro - Word Embeddings, Word2Vec, RNNs Lecture 117 Word2Vec Lecture 118 RNNs Lecture 119 Transformers and LLMs Lecture 120 LLMs Lecture 121 Early LLMs Lecture 122 Cloud-Based LLMs Lecture 123 Evolution of GenAI Section 13: Module 3 - Risk and Risk Factors Lecture 124 Introduction Lecture 125 Bias and Fairness Lecture 126 Group Fairness Lecture 127 Individual Fairness Lecture 128 Demographic Parity Lecture 129 Confusion Matrix Lecture 130 Predictive Rate Parity Lecture 131 Impossibility and Trade Offs Lecture 132 Equal Opportunities Lecture 133 Sources of Unfairness Lecture 134 Data Collection and Composition Lecture 135 Model Development Lecture 136 Model Development Lecture 137 Explainability, Interpretability, and Transparency Lecture 138 Black Box Problem Lecture 139 Opaqueness Lecture 140 Explainable AI (XAI) Lecture 141 Autonomy and Manipulation Lecture 142 Safety and Well-Being Lecture 143 Reputational Risks Lecture 144 Existential Risks Lecture 145 Global Challenges and Risks Lecture 146 Misinformation Campaigns Section 14: Module 4 Lecture 147 Introduction - Responsible and Ethical AI Lecture 148 Practical Ethics Lecture 149 Ethical Frameworks Lecture 150 Deontology Lecture 151 Virtue Ethics Lecture 152 What can AI Ethics learn from Medical Ethics Lecture 153 Principles of AI Ethics Lecture 154 Bias and Discrimination Lecture 155 Fairness in AI Systems Lecture 156 Privacy and Cybersecurity Lecture 157 Governance Challenges Lecture 158 GC 2: Lack of AI Ethics Structures, Lack of Regulations Lecture 159 GC 3: Unpredictability Issues, Lack of Truth Tracking Abilities, & Privacy Section 15: Module 5: Data and AI Model Governance Lecture 160 Intro - Data and AI Model Governance Lecture 161 Data Governance Lecture 162 Data Provenance Lecture 163 Data Classification and Metadata Management Lecture 164 Data Protection, Security, & Compliance Lecture 165 Board Roles and Responsibilities Lecture 166 Model Governance Lecture 167 Model Development and Testing Lecture 168 Testing Responsibilities Lecture 169 Use Test in QRM Lecture 170 Model Validation in QRMs Lecture 171 Model Governance Policies Lecture 172 Model Inventory and Landscape Lecture 173 Model Validation Overview Lecture 174 Model Design Lecture 175 Numerical and Statistical Issues - Discretization Lecture 176 Approximation Lecture 177 Numerical Evaluation in QRMs Lecture 178 Random Numbers Lecture 179 Implementation, Software, and Data Lecture 180 Processes and Misinterpretation I Lecture 181 Processes and Misinterpretation II Finance and risk professionals seeking to understand how AI is transforming risk management and aiming to earn the GARP Risk and AI (RAI) certification.,Compliance officers, auditors, and regulators who need a structured understanding of the risks and governance challenges posed by AI-driven systems in financial institutions.,Students, career switchers, and early-career professionals interested in entering the intersection of finance, risk, and emerging technologies-no prior AI or deep finance knowledge required. 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