Gain foundational knowledge of Artificial Intelligence including:

• What AI is and its business value
• Types of AI techniques and real-world applications
• Overview of popular AI tools and frameworks
• How AI drives digital transformation

Learn the basics of Artificial Intelligence and build your first AI model:

• Installing and setting up AI development tools
• Exploring the user interface of popular AI platforms
• Creating your first AI project
• Using datasets, models, and workflows effectively

Dive into AI model optimization using feature selection techniques:

• Understanding and refining feature selection methods
• Using filters, wrappers, and embedded approaches
• Troubleshooting overfitting and data imbalance issues
• Best practices in AI model optimization

Learn how to prepare and manipulate data effectively for AI workflows:

• Understanding variables, parameters, and data types in AI pipelines
• Working with text, numerical, and categorical data
• Handling tabular datasets and spreadsheet inputs
• Applying data cleaning, transformation, and normalization

This lesson focuses on extracting and managing data for AI-driven applications:

• Advanced techniques for handling dynamic web content
• Collecting structured and unstructured data from online sources
• Managing API connections, authentication, and sessions
• Strategies to handle restricted or secured data sources
• Scaling and optimizing large-scale data gathering for AI modelsg

Master essential techniques to make AI workflows more reliable and efficient:

• Applying structured error-handling in AI pipelines
• Defining and managing custom exception scenarios in model execution
• Using logging frameworks for traceability and experiment tracking
• Debugging model training and inference with checkpoints and breakpoints
• Profiling performance to optimize computation, memory, and latency

Learn how to manage and automate AI workflows at an enterprise scale:

• Setting up and managing task queues for AI pipelines
• Securing credentials, APIs, and data assets
• Scheduling jobs and prioritizing model training or inference tasks
• Monitoring pipeline performance and configuring alerts
• Best practices for deploying AI solutions across multiple environments

Develop a complete end-to-end AI solution covering:

• Gathering requirements and understanding the problem domain
• Designing the AI solution architecture and workflow
• Implementing all learned techniques: data preprocessing, modeling, and evaluation
• Documenting methodology and ensuring knowledge transfer
• Deploying the AI solution and planning for maintenance and monitoring