AI & ML Algorithms and their Practical Applications

• Lesson 6 Deep Learning/004. 6.3 Convolutional Neural Networks (CNN) for Image Recognition.mp4-94.28 MB
• Lesson 2 Unsupervised Learning/003. 2.2 How K-means Works, Advantages and Limitations.mp4-73.34 MB
• Lesson 6 Deep Learning/002. 6.1 Why is this Learning Deep .mp4-73.24 MB
• Lesson 4 Random Forests/003. 4.2 Build Your First Tree.mp4-52.38 MB
• Lesson 6 Deep Learning/003. 6.2 Artificial Neural Networks (ANN) step-by-step.mp4-50.21 MB
• Lesson 7 An Introduction to Large Language Models/003. 7.2 Word Embedding.mp4-40.75 MB
• Lesson 7 An Introduction to Large Language Models/004. 7.3 Transformers.mp4-38.74 MB
• Lesson 2 Unsupervised Learning/005. 2.4 DBSCAN for Complex Shapes.mp4-31.76 MB
• Lesson 5 Reinforcement Learning/003. 5.2 Understanding Reinforcement Learning Components and Framework.mp4-30.56 MB
• Lesson 7 An Introduction to Large Language Models/005. 7.4 Advanced Topics.mp4-30.45 MB
• Lesson 2 Unsupervised Learning/004. 2.3 Hierarchical Clustering.mp4-29.78 MB
• Introduction/001. AI and ML Algorithm Foundations Introduction.mp4-29.6 MB
• Lesson 7 An Introduction to Large Language Models/002. 7.1 How did Large Language Models (LLMs) Develop.mp4-29.29 MB
• Lesson 5 Reinforcement Learning/005. 5.4 Q-Learning.mp4-28.76 MB
• Lesson 1 An Introduction to the World of Artificial Intelligence and Machine Learning/003. 1.2 AI and ML Definitions.mp4-28.09 MB
• Lesson 2 Unsupervised Learning/002. 2.1 Clustering Principles.mp4-25.64 MB
• Lesson 3 Supervised Learning/003. 3.2 Linear Regression Fitting a Curve with Training Data.mp4-24.8 MB
• Lesson 3 Supervised Learning/008. 3.7 Classification 2 - Support Vector Machines (SVM).mp4-24.34 MB
• Lesson 4 Random Forests/004. 4.3 Build a Full Forest.mp4-21.61 MB
• Lesson 3 Supervised Learning/005. 3.4 Gradient Descent.mp4-20.45 MB
• Lesson 3 Supervised Learning/002. 3.1 Predictive Functions.mp4-15.49 MB
• Lesson 3 Supervised Learning/007. 3.6 Classification 1 Logistical Regression.mp4-15.07 MB
• Lesson 1 An Introduction to the World of Artificial Intelligence and Machine Learning/002. 1.1 A Brief History of AI and ML.mp4-14.66 MB
• Lesson 3 Supervised Learning/006. 3.5 The Machine Learning Workflow.mp4-13.49 MB
• Lesson 4 Random Forests/002. 4.1 Why Use Trees.mp4-13.38 MB
• Lesson 5 Reinforcement Learning/002. 5.1 Why Reinforcement Learning.mp4-13.13 MB
• Lesson 1 An Introduction to the World of Artificial Intelligence and Machine Learning/004. 1.3 Discriminative vs. Generative AI.mp4-12.08 MB
• Summary/001. AI and ML Algorithm Foundations Summary.mp4-10.44 MB
• Lesson 5 Reinforcement Learning/004. 5.3 The Bellman Value Equation.mp4-10.26 MB
• Lesson 2 Unsupervised Learning/001. Learning objectives.mp4-8.22 MB
• Lesson 4 Random Forests/001. Learning objectives.mp4-7.01 MB
• Lesson 6 Deep Learning/001. Learning objectives.mp4-6.65 MB
• Lesson 5 Reinforcement Learning/001. Learning objectives.mp4-5.18 MB
• Lesson 7 An Introduction to Large Language Models/001. Learning objectives.mp4-4.5 MB
• Lesson 3 Supervised Learning/001. Learning objectives.mp4-4.14 MB
• Lesson 3 Supervised Learning/004. 3.3 The Cost Function.mp4-4.02 MB
• Lesson 1 An Introduction to the World of Artificial Intelligence and Machine Learning/001. Learning objectives.mp4-2.86 MB