Model development frameworks and their applications explained

1- Introduction to Data Management in AI/ML 2- Overview of data sources 3- Methods for Acquiring Data 4- Importance of Data Cleaning and Preprocessing 5- Hear from an Expert: The Value of Consistent Taxonomy 6- Introduction to RAG 7- Best Practices for Maintaining Efficient Data Sources for RAG 8- Hear from an Expert: Security Considerations When Working with Data 9- Summary: Data Management in AI/ML 10- Hear from an Expert: Industry Exemplar 11- Walkthrough: Setting up your environment in Microsoft Azure (Optional) 12- Selecting the right model deployment strategy in Microsoft Azure 13- Walkthrough: Justifying your choice of model selection (Optional) 14- Introduction to Machine Learning Models 15- Course syllabus: Foundations of AI and Machine Learning Infrastructure 16- The structure and role of data sources and pipelines explained 17- Supervised vs Unsupervised Learning Models 18- In-depth exploration of data sources and pipelines 19- Understanding Regression Models in Detail 20- Model development frameworks and their applications explained 21- Key considerations in selecting a model development framework 22- Understanding Classification Models in Detail 23- Clustering and Unsupervised Learning Techniques 24- Model Selection Strategies 25- Introduction to Scikit-learn 26- Introduction to TensorFlow and PyTorch 27- Model Training and Validation 28- Evaluating and Comparing Machine Learning Models 29- Introduction to Considerations when deploying platforms 30- Best Practices for Packaging and Containerizing Models 31- Tools and Frameworks for Model Deployment 32- Instructions: Preparing a Model for Deployment 33- Tools and Practices for Version Control (Git, DVC) 34- Implementing Version Control for Reproducibility 35- End-to-End Machine Learning Project Workflow 36- Case Study: Building a Recommendation System 37- Case Study: Spam Detection System 38- Real-World Challenges in Machine Learning 39- Criteria for Evaluating Deployment Platforms 40- Capstone Project: Build Your Own ML Solution 41- Real-world Case Studies of Successful AI/ML Deployments