Full Stack AI Engineering

Basic ML Algorithms: Overview of fundamental machine learning algorithms. 

Data Preprocessing: Techniques for preparing data for machine learning. 

Model Evaluation: Methods for assessing the performance of ML models. 

Deep Learning: Introduction to deep learning concepts and neural networks. 

Neural Networks: Understanding the architecture and functioning of neural networks. 

Advanced ML Techniques: Exploring advanced machine learning methods and algorithms. 

API Development: Techniques for building APIs for AI applications. 

Data Handling: Managing and processing data in backend systems. 

Integrating ML Models in the Backend: Techniques for incorporating machine learning models into backend applications. 

Building User Interfaces: Techniques for developing user interfaces for AI applications. 

Integrating AI Models in the Frontend: Methods for incorporating AI functionalities into frontend applications. 

Deployment Strategies: Best practices for deploying AI applications. 

Cloud Services for AI: Utilizing cloud platforms for AI deployment. 

Scaling AI Applications: Techniques for scaling AI applications to handle larger datasets and higher traffic. 

Ethical Considerations in AI: Understanding the ethical implications of AI technologies. 

Data Privacy: Best practices for handling and protecting user data in AI applications. 

Responsible AI Practices: Developing AI applications responsibly and ethically.

End-to-End Development: Techniques for developing a full-stack AI application from start to finish. 

Project Management: Best practices for managing AI project development.