| 37 - 1. Hands-on AutoGen.mp4 | 31.6 MB | ||
| 38 - 2. Hands-on AutoGen.mp4 | 87.9 MB | ||
| 39 - 3. Hands-on IBM Bee Framework.mp4 | 126.6 MB | ||
| 40 - 4. Hands-on LangGraph.mp4 | 117.2 MB | ||
| 41 - 5. Hands-on CrewAI.mp4 | 97 MB | ||
| 42 - 6. Hands-on AutoGPT.mp4 | 45.3 MB | ||
| 44 - 2. Overview of MLOps and its Importance.mp4 | 3.1 MB | ||
| 45 - 3. Evolution of Machine Learning Operations.mp4 | 2.8 MB | ||
| 46 - 4. Key Concepts in MLOps - Versioning, Automation, and Monitoring.mp4 | 4 MB | ||
| 47 - 5. MLOps vs. DevOps - Similarities and Differences.mp4 | 3.3 MB | ||
| 48 - 6. Hands-on - Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline.mp4 | 182.3 MB | ||
| 49 - 7. Introduction to Data Science to Production Pipeline Section.mp4 | 1.4 MB | ||
| 50 - 8. Overview of the ML Workflow - Data Preparation to Deployment.mp4 | 7.5 MB | ||
| 51 - 9. Experimentation vs. Production.mp4 | 5.4 MB | ||
| 52 - 10. Challenges in Deploying ML Models.mp4 | 2.2 MB | ||
| 53 - 11. Hands-on - Build an end-to-end pipeline for an ML model.mp4 | 250.1 MB | ||
| 55 - 13. Introduction to Cloud Platforms (AWS, GCP, Azure).mp4 | 12.7 MB | ||
| 56 - 14. Containerization with Docker.mp4 | 3.6 MB | ||
| 57 - 15. Kubernetes for Orchestrating ML Workloads.mp4 | 3.5 MB | ||
| 58 - 16. Setting up Local MLOps Environments.mp4 | 3.7 MB | ||
| 59 - 17. Hands-on - Containerize simple ML model & deploy it locally using Kubernetes.mp4 | 442.6 MB | ||
| 60 - Congratulations and Best of Luck.mp4 | 16.8 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 24 total files | |||
AI Engineer Professional Certificate Course
https://WebToolTip.com
Published 6/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 13m | Size: 1.41 GB
Master Deep Learning, Transformers, MLOps & AI Agent Development with Real-World Projects
What you'll learn
Tune and optimize machine learning models using advanced techniques
Build and train CNNs for image classification and computer vision tasks
Develop RNNs, LSTMs, and GRUs for time series and sequence modeling
Understand and implement transformers and attention mechanisms
Apply transfer learning to fine-tune powerful pre-trained models
Design and analyze AI agents for autonomous decision-making
Use TensorFlow and PyTorch for deep learning projects
Deploy models using MLOps tools like Docker, MLflow, and CI/CD pipelines
Requirements
Completion of a beginner or associate-level AI or machine learning course (or equivalent knowledge)
Strong understanding of Python programming, including experience with functions, classes, and working with libraries like NumPy and Pandas
Solid grasp of basic machine learning concepts, including regression, classification, model evaluation, and overfitting
Familiarity with deep learning fundamentals, including neural networks and basic model architecture
Prior exposure to tools like Jupyter Notebook, TensorFlow, or PyTorch
Working knowledge of mathematics for AI, including linear algebra, probability, and calculus
A computer (Windows, macOS, or Linux) with reliable internet and the ability to install development tools
Willingness to explore complex, production-grade systems and invest time in hands-on coding, model experimentation, and deployment workflows
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 3.5 GB | freecoursewb | 3 days | 71 | 18 | |
| 1.1 GB | freecoursewb | 3 days | 40 | 6 | |
| 483.5 MB | freecoursewb | 1 week | 15 | 3 | |
| 2.8 GB | freecoursewb | 1 week | 46 | 16 | |
| 2.3 GB | freecoursewb | 1 week | 1 | 2 |
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