10-15-2024, 02:30 PM
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)
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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