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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