Master AI & ML Algorithms: Complete 2024 Pro Guide
Focused View
4:10:48
001. AI and ML Algorithm Foundations Introduction.mp4
05:16
002. AI and ML Algorithm Foundations Introduction.mp4
05:16
001. Learning objectives.mp4
00:34
002. 1.1 A Brief History of AI and ML.mp4
04:13
003. 1.2 AI and ML Definitions.mp4
07:47
004. 1.3 Discriminative vs. Generative AI.mp4
03:28
001. Learning objectives.mp4
01:20
002. 2.1 Clustering Principles.mp4
05:14
003. 2.2 How K-means Works, Advantages and Limitations.mp4
16:12
004. 2.3 Hierarchical Clustering.mp4
08:07
005. 2.4 DBSCAN for Complex Shapes.mp4
07:47
001. Learning objectives.mp4
00:48
002. 3.1 Predictive Functions.mp4
04:18
003. 3.2 Linear Regression Fitting a Curve with Training Data.mp4
08:48
004. 3.3 The Cost Function.mp4
01:15
005. 3.4 Gradient Descent.mp4
05:59
006. 3.5 The Machine Learning Workflow.mp4
04:00
007. 3.6 Classification 1 Logistical Regression.mp4
04:23
008. 3.7 Classification 2 - Support Vector Machines (SVM).mp4
06:51
001. Learning objectives.mp4
01:11
002. 4.1 Why Use Trees.mp4
03:16
003. 4.2 Build Your First Tree.mp4
15:08
004. 4.3 Build a Full Forest.mp4
06:25
001. Learning objectives.mp4
01:00
002. 5.1 Why Reinforcement Learning.mp4
03:33
003. 5.2 Understanding Reinforcement Learning Components and Framework.mp4
09:08
004. 5.3 The Bellman Value Equation.mp4
03:06
005. 5.4 Q-Learning.mp4
08:34
001. Learning objectives.mp4
01:06
002. 6.1 Why is this Learning Deep .mp4
17:46
003. 6.2 Artificial Neural Networks (ANN) step-by-step.mp4
11:46
004. 6.3 Convolutional Neural Networks (CNN) for Image Recognition.mp4
23:29
001. Learning objectives.mp4
00:53
002. 7.1 How did Large Language Models (LLMs) Develop.mp4
09:04
003. 7.2 Word Embedding.mp4
10:45
004. 7.3 Transformers.mp4
11:48
005. 7.4 Advanced Topics.mp4
09:20
001. AI and ML Algorithm Foundations Summary.mp4
01:54
More details
Course Overview
This comprehensive course dives deep into Artificial Intelligence and Machine Learning algorithms, from foundational concepts to advanced techniques like Deep Learning and Large Language Models. Gain practical skills through 7 structured lessons.
What You'll Learn
- Core differences between discriminative and generative AI
- Supervised vs. unsupervised learning techniques
- How to implement neural networks and reinforcement learning
Who This Is For
- Aspiring data scientists building ML foundations
- Developers transitioning into AI roles
- Tech professionals upgrading their algorithm skills
Key Benefits
- Hands-on experience with K-means, SVMs, and Random Forests
- Understand cutting-edge LLMs and Transformers
- Master the complete ML workflow from theory to application
Curriculum Highlights
- AI/ML Foundations & History
- Clustering Techniques & Supervised Learning
- Deep Learning & Large Language Models
Focused display
Category
- language english
- Training sessions 38
- duration 4:10:48
- Release Date 2025/05/26
