| 1 -Section Intro.mp4 | 23.9 MB | ||
| 1 -What is Agentic AI.mp4 | 13.1 MB | ||
| 1 -What is MLOps.mp4 | 353.6 MB | ||
| 2 -Linear Regression.mp4 | 50.3 MB | ||
| 2 -Story of Evolution of MLOps, LLMOps and AgenticAIOps.mp4 | 308.5 MB | ||
| 2 -Traditional Programming vs Machine Learning.mp4 | 131.2 MB | ||
| 2 -What and How of Large Language Models (LLMs).mp4 | 47.6 MB | ||
| 2 -What is Agentic AI.mp4 | 173.8 MB | ||
| 3 -Comparing Three Approach to AI - ML, LLM, Agentic.mp4 | 351 MB | ||
| 3 -Goals and Tools.mp4 | 82 MB | ||
| 3 -Logistic Regression.mp4 | 26.6 MB | ||
| 3 -Supervised Learning.mp4 | 76.6 MB | ||
| 3 -Tokens and Parameters.mp4 | 45.5 MB | ||
| 4 -Decision Tree.mp4 | 28.2 MB | ||
| 4 -MLOps Case Studies - Learning from the Pioneers.mp4 | 124 MB | ||
| 4 -Memory and Planning.mp4 | 34.1 MB | ||
| 4 -Transformers and Attention Mechanism.mp4 | 47 MB | ||
| 4 -Unsupervised Learning.mp4 | 76.6 MB | ||
| 5 -Agentic Architecture and Types of Agents.mp4 | 100.5 MB | ||
| 5 -Comparing Devops vs MLOps.mp4 | 286.8 MB | ||
| 5 -Pre Training and Fine Tuning of Foundational Models.mp4 | 35.5 MB | ||
| 5 -Random Forest.mp4 | 39.3 MB | ||
| 5 -Reinforcement Learning.mp4 | 74.8 MB | ||
| 6 -Emergence of a MLOps Engineer.mp4 | 229.8 MB | ||
| 6 -Feature Engineering and Dataset Splitting.mp4 | 53.9 MB | ||
| 6 -Multi Agents, Examples and Challenges.mp4 | 97.2 MB | ||
| 6 -Prompt Engineering.mp4 | 69.4 MB | ||
| 6 -Support Vector Machines (SVMs).mp4 | 25.3 MB | ||
| 7 -Agentic Devops, MLOps and Ethical Considerations.mp4 | 43.2 MB | ||
| 7 -Limitations of LLMs.mp4 | 33.4 MB | ||
| 7 -Neural Networks.mp4 | 45.2 MB | ||
| 8 -Boosting Algorithms.mp4 | 84.6 MB | ||
| 8 -How fit is your Model Evaluation and OverUnderfitting.mp4 | 47.3 MB | ||
| 8 -Module Summary.mp4 | 15.3 MB | ||
| 8 -Retrieval Augmented Generation (RAG).mp4 | 15.6 MB | ||
| 9 -Hyperparameter Tuning and Balancing BiasVariance.mp4 | 58.5 MB | ||
| 9 -Module Summary.mp4 | 29.7 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 41 total files | |||
AI/ML Foundations for Absolute Beginners (AgenticAI + MLOps)
https://WebToolTip.com
Published 3/2025
Created by Gourav Shah,School of Devops & AI,Vivian Aranha
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 40 Lectures ( 4h 16m ) | Size: 3.35 GB
Build a Solid Conceptual Foundation on Machine Learning, Large Language Models (LLMs) and Agentic AI along with MLOps
What you'll learn
Learn What Machine Learning Really Is – Build a clear conceptual understanding of how machine learning works and how it differs from traditional programming.
Explore Key Types of Machine Learning – Get introduced to supervised, unsupervised, and reinforcement learning with simple, relatable explanations.
Understand the End-to-End ML Process – Learn the basic steps involved in building a machine learning model, from data preparation to evaluation.
Get Familiar with Popular ML Algorithms – Discover widely-used ML algorithms like linear regression, decision trees, and neural networks—without diving into hea
Introduction to Large Language Models (LLMs) – Understand how modern AI models like ChatGPT work, including concepts like tokens, transformers, and prompt engin
Discover the fundamentals of Agentic AI – including memory, planning, agent architectures, tools, and real-world use cases
Reflect on the ethical considerations of Agentic AI and how it ties into modern practices like DevOps and MLOps
Requirements
No programming or math background required. This course is designed for complete beginners. All concepts will be explained from scratch in a simple, intuitive way.
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