Oreilly – AI & ML Algorithms and their Practical Applications 2024-9

AI & ML Algorithms and their Practical Applications course. This comprehensive course takes you into the exciting world of artificial intelligence and machine learning. In this course, you have familiarized yourself with the basic concepts of artificial intelligence and machine learning and learned different algorithms to solve different problems. Also, you have learned about the practical applications of these algorithms in the real world.

What you will learn:

• Basics of artificial intelligence and machine learning
• Different types of learning (unsupervised, supervised, reinforcement)
• Key algorithms such as K-means, logistic regression, SVM, random forests, neural networks and transformers
• Practical applications of artificial intelligence in the real world
• The latest developments in the field of artificial intelligence

This course is suitable for people who:

• They are looking for a complete understanding of the concepts of artificial intelligence and machine learning.
• They are interested in learning different algorithms and their applications.
• They want to have a professional activity in the field of artificial intelligence and machine learning.
• They want to know the latest developments in this field.
• They intend to use this knowledge to solve real problems.
• Students, researchers and data professionals who want to improve their skills in this field.

Course specifications AI & ML Algorithms and their Practical Applications

• Publisher: Oreilly
• Instructor: Robert Barton, Jerome Henry
• Training level: beginner to advanced
• Training duration: 4 hours and 5 minutes

Course headings

• Introduction
• AI and ML Algorithm Foundations: Introduction
• Lesson 1: An Introduction to the World of Artificial Intelligence and Machine Learning
• Learning objectives
• 1.1 A Brief History of AI and ML
• 1.2 AI and ML Definitions
• 1.3 Discriminative vs. Generative AI
• Lesson 2: Unsupervised Learning
• Learning objectives
• 2.1 Clustering Principles
• 2.2 How K-means Works, Advantages and Limitations
• 2.3 Hierarchical Clustering
• 2.4 DBSCAN for Complex Shapes
• Lesson 3: Supervised Learning
• Learning objectives
• 3.1 Predictive Functions
• 3.2 Linear Regression – Fitting a Curve with Training Data
• 3.3 The Cost Function
• 3.4 Gradient Descent
• 3.5 The Machine Learning Workflow
• 3.6 Classification 1 – Logistical Regression
• 3.7 Classification 2 – Support Vector Machines (SVM)
• Lesson 4: Random Forests
• Learning objectives
• 4.1 Why Use Trees?
• 4.2 Build Your First Tree
• 4.3 Build a Full Forest
• Lesson 5: Reinforcement Learning
• Learning objectives
• 5.1 Why Reinforcement Learning
• 5.2 Understanding Reinforcement Learning Components and Framework
• 5.3 The Bellman Value Equation
• 5.4 Q-Learning
• Lesson 6: Deep Learning
• Learning objectives
• 6.1 Why is this learning “Deep”?
• 6.2 Artificial Neural Networks (ANN) step-by-step
• 6.3 Convolutional Neural Networks (CNN) for Image Recognition
• Lesson 7: An Introduction to Large Language Models
• Learning objectives
• 7.1 How did Large Language Models (LLMs) Develop?
• 7.2 Word Embedding
• 7.3 Transformers
• 7.4 Advanced Topics
• Summary
• AI and ML Algorithm Foundations: Summary

Course images

Sample video of the course

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