AI & ML Algorithms and their Practical Applications

سرفصل های دوره

Introduction

001. AI and ML Algorithm Foundations Introduction

002. AI and ML Algorithm Foundations Introduction

Lesson 1 An Introduction to the World of Artificial Intelligence and Machine Learning

001. Learning objectives

002. 1.1 A Brief History of AI and ML

003. 1.2 AI and ML Definitions

004. 1.3 Discriminative vs. Generative AI

Lesson 2 Unsupervised Learning

001. Learning objectives

002. 2.1 Clustering Principles

003. 2.2 How K-means Works, Advantages and Limitations

004. 2.3 Hierarchical Clustering

005. 2.4 DBSCAN for Complex Shapes

Lesson 3 Supervised Learning

001. Learning objectives

002. 3.1 Predictive Functions

003. 3.2 Linear Regression Fitting a Curve with Training Data

004. 3.3 The Cost Function

005. 3.4 Gradient Descent

006. 3.5 The Machine Learning Workflow

007. 3.6 Classification 1 Logistical Regression

008. 3.7 Classification 2 - Support Vector Machines (SVM)

Lesson 4 Random Forests

001. Learning objectives

002. 4.1 Why Use Trees

003. 4.2 Build Your First Tree

004. 4.3 Build a Full Forest

Lesson 5 Reinforcement Learning

001. Learning objectives

002. 5.1 Why Reinforcement Learning

003. 5.2 Understanding Reinforcement Learning Components and Framework

004. 5.3 The Bellman Value Equation

005. 5.4 Q-Learning

Lesson 6 Deep Learning

001. Learning objectives

002. 6.1 Why is this Learning Deep

003. 6.2 Artificial Neural Networks (ANN) step-by-step

004. 6.3 Convolutional Neural Networks (CNN) for Image Recognition

Lesson 7 An Introduction to Large Language Models

001. Learning objectives

002. 7.1 How did Large Language Models (LLMs) Develop

003. 7.2 Word Embedding

004. 7.3 Transformers

005. 7.4 Advanced Topics

Summary

001. AI and ML Algorithm Foundations Summary

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تولید کننده: LiveLessons