Course description

The AI Engineer course is designed to equip learners with the essential skills required to design, build, and deploy AI-based systems. The course blends core concepts in machine learning and deep learning with hands-on practice in building scalable AI models using popular frameworks. Learners will explore real-world use cases, ethical concerns, and the integration of AI with cloud infrastructure. It is ideal for aspiring AI professionals seeking applied experience in developing intelligent systems.

Learning Objectives:

· Understand machine learning concepts, models, and evaluation techniques

· Implement supervised and unsupervised learning algorithms

· Build and optimize neural networks using TensorFlow or PyTorch

· Engineer and preprocess data for AI applications

· Deploy AI models using cloud services and APIs

· Analyze the ethical, social, and legal implications of AI

Target Audience:

· Data analysts and software developers transitioning into AI

· Computer science graduates seeking AI expertise

· Professionals aiming to enter the AI/ML industry

Course Duration: 12 weeks (part-time or full-time intensive options)

Delivery Format: Blended or fully online with project-based labs, video lectures, and instructor support

Modules/Units:

1. Introduction to AI and Machine Learning

2. Data Preparation and Feature Engineering

3. Supervised and Unsupervised Learning

4. Deep Learning and Neural Networks

5. Natural Language Processing and Computer Vision

6. AI Model Evaluation and Optimization

7. Deployment with Flask, FastAPI, and Cloud Services

8. AI Ethics and Policy Considerations

Assessment Methods:

· Quizzes and coding assignments

· Final capstone project (real-world AI application)

· Oral presentation and model evaluation report

What will I learn?

• Develop and deploy AI systems in production
• Build machine learning pipelines
• Transition into roles such as AI Engineer, ML Engineer, or Data Scientist