Intermediate
AI & ML
Computer Vision Basics
This comprehensive course will take you from beginner to confident practitioner in Computer Vision Basics. You'll learn industry-standard techniques and best practices used by professionals worldwide.
Through hands-on projects and real-world examples, you'll gain the skills needed to succeed in today's competitive tech landscape. Our curriculum is designed by industry experts with years of experience.
By the end of this course, you'll have a portfolio of projects to showcase your abilities and the confidence to apply your knowledge in professional settings.
Course Syllabus
Week 1: Introduction to AI Concepts
- Lesson 1: AI and ML landscape overview Preview 18min
- Lesson 2: Supervised vs unsupervised learning Preview 19min
- Lesson 3: Prepare training data 18min
- Lesson 4: Train a baseline model 9min
Week 2: Data Preparation & Cleaning
- Lesson 1: Feature engineering practice 9min
- Lesson 2: Neural network intuition 21min
- Lesson 3: Work with pretrained models 19min
- Lesson 4: Prompting and LLM basics 20min
- Lesson 5: Guardrails and evaluation 8min
Week 3: Building Your First Model
- Lesson 1: Feature engineering practice 18min
- Lesson 2: Neural network intuition 25min
- Lesson 3: Work with pretrained models 15min
Week 4: Training & Evaluation
- Lesson 1: Feature engineering practice 20min
- Lesson 2: Neural network intuition 21min
- Lesson 3: Work with pretrained models 8min
- Lesson 4: Prompting and LLM basics 11min
- Lesson 5: Guardrails and evaluation 23min
- Lesson 6: Build a small AI project 22min
Week 5: Advanced Algorithms
- Lesson 1: Feature engineering practice 17min
- Lesson 2: Neural network intuition 21min
- Lesson 3: Work with pretrained models 17min
- Lesson 4: Prompting and LLM basics 20min
- Lesson 5: Guardrails and evaluation 22min
- Lesson 6: Build a small AI project 24min
Week 6: Neural Networks
- Lesson 1: Feature engineering practice 8min
- Lesson 2: Neural network intuition 14min
- Lesson 3: Work with pretrained models 21min
- Lesson 4: Prompting and LLM basics 18min
Week 7: Model Deployment
- Lesson 1: Feature engineering practice 22min
- Lesson 2: Neural network intuition 17min
- Lesson 3: Work with pretrained models 10min
- Lesson 4: Prompting and LLM basics 23min
- Lesson 5: Guardrails and evaluation 12min
Week 8: Real-World Applications
- Lesson 1: Feature engineering practice 24min
- Lesson 2: Neural network intuition 19min
- Lesson 3: Work with pretrained models 23min