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Oreilly - AI & ML Algorithms and Their Practical Applications - AD-TEAM - 10-15-2024 968.26 MB | 00:23:29 | mp4 | 1280X720 | 16:9 Genre:eLearning |Language:English
Files Included :
001 AI and ML Algorithm Foundations Introduction (29.6 MB) 002 AI and ML Algorithm Foundations Introduction (29.6 MB) 001 Learning objectives (2.86 MB) 002 1 1 A Brief History of AI and ML (14.66 MB) 003 1 2 AI and ML Definitions (28.09 MB) 004 1 3 Discriminative vs Generative AI (12.08 MB) 001 Learning objectives (8.22 MB) 002 2 1 Clustering Principles (25.64 MB) 003 2 2 How K-means Works, Advantages and Limitations (73.34 MB) 004 2 3 Hierarchical Clustering (29.78 MB) 005 2 4 DBSCAN for Complex Shapes (31.76 MB) 001 Learning objectives (4.14 MB) 002 3 1 Predictive Functions (15.49 MB) 003 3 2 Linear Regression Fitting a Curve with Training Data (24.8 MB) 004 3 3 The Cost Function (4.02 MB) 005 3 4 Gradient Descent (20.45 MB) 006 3 5 The Machine Learning Workflow (13.49 MB) 007 3 6 Classification 1 Logistical Regression (15.07 MB) 008 3 7 Classification 2 - Support Vector Machines (SVM) (24.34 MB) 001 Learning objectives (7.01 MB) 002 4 1 Why Use Trees (13.38 MB) 003 4 2 Build Your First Tree (52.38 MB) 004 4 3 Build a Full Forest (21.61 MB) 001 Learning objectives (5.18 MB) 002 5 1 Why Reinforcement Learning (13.13 MB) 003 5 2 Understanding Reinforcement Learning Components and Framework (30.56 MB) 004 5 3 The Bellman Value Equation (10.26 MB) 005 5 4 Q-Learning (28.76 MB) 001 Learning objectives (6.65 MB) 002 6 1 Why is this Learning Deep (73.24 MB) 003 6 2 Artificial Neural Networks (ANN) step-by-step (50.21 MB) 004 6 3 Convolutional Neural Networks (CNN) for Image Recognition (94.28 MB) 001 Learning objectives (4.5 MB) 002 7 1 How did Large Language Models (LLMs) Develop (29.29 MB) 003 7 2 Word Embedding (40.75 MB) 004 7 3 Transformers (38.74 MB) 005 7 4 Advanced Topics (30.45 MB) 001 AI and ML Algorithm Foundations Summary (10.44 MB)
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