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1 -Section Intro.mp4 |
36.90MB |
1 -Section Intro.mp4 |
16.37MB |
1 -Section Intro.mp4 |
23.85MB |
1 -What is Agentic AI.mp4 |
13.06MB |
1 -What is MLOps.mp4 |
353.61MB |
2 -Linear Regression.mp4 |
50.28MB |
2 -Story of Evolution of MLOps, LLMOps and AgenticAIOps.mp4 |
308.49MB |
2 -Traditional Programming vs Machine Learning.mp4 |
131.20MB |
2 -What and How of Large Language Models (LLMs).mp4 |
47.58MB |
2 -What is Agentic AI.mp4 |
173.79MB |
3 -Comparing Three Approach to AI - ML, LLM, Agentic.mp4 |
351.02MB |
3 -Goals and Tools.mp4 |
82.04MB |
3 -Logistic Regression.mp4 |
26.63MB |
3 -Supervised Learning.mp4 |
76.57MB |
3 -Tokens and Parameters.mp4 |
45.50MB |
4 -Decision Tree.mp4 |
28.24MB |
4 -Memory and Planning.mp4 |
34.05MB |
4 -MLOps Case Studies - Learning from the Pioneers.mp4 |
123.96MB |
4 -Transformers and Attention Mechanism.mp4 |
46.98MB |
4 -Unsupervised Learning.mp4 |
76.62MB |
5 -Agentic Architecture and Types of Agents.mp4 |
100.51MB |
5 -Comparing Devops vs MLOps.mp4 |
286.81MB |
5 -Pre Training and Fine Tuning of Foundational Models.mp4 |
35.54MB |
5 -Random Forest.mp4 |
39.30MB |
5 -Reinforcement Learning.mp4 |
74.77MB |
6 -Emergence of a MLOps Engineer.mp4 |
229.76MB |
6 -Feature Engineering and Dataset Splitting.mp4 |
53.89MB |
6 -Multi Agents, Examples and Challenges.mp4 |
97.20MB |
6 -Prompt Engineering.mp4 |
69.38MB |
6 -Support Vector Machines (SVMs).mp4 |
25.29MB |
7 -Agentic Devops, MLOps and Ethical Considerations.mp4 |
43.20MB |
7 -Limitations of LLMs.mp4 |
33.41MB |
7 -Neural Networks.mp4 |
45.23MB |
8 -Boosting Algorithms.mp4 |
84.65MB |
8 -How fit is your Model Evaluation and OverUnderfitting.mp4 |
47.33MB |
8 -Module Summary.mp4 |
15.25MB |
8 -Retrieval Augmented Generation (RAG).mp4 |
15.64MB |
9 -Hyperparameter Tuning and Balancing BiasVariance.mp4 |
58.48MB |
9 -Module Summary.mp4 |
29.69MB |
Bonus Resources.txt |
70B |
Get Bonus Downloads Here.url |
180B |