AI
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.\\n\\nThrough 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.\\n\\nBy 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: Gradient Descent Best Practices Preview 18min
  • Lesson 2: Understanding Neurons Preview 19min
  • Lesson 3: Mastering Tensors 18min
  • Lesson 4: Feature Engineering Fundamentals 9min

Week 2: Data Preparation & Cleaning

  • Lesson 1: Advanced Gradient Descent Techniques 9min
  • Lesson 2: Tensors Fundamentals 21min
  • Lesson 3: Tensors Best Practices 19min
  • Lesson 4: Overfitting Fundamentals 20min
  • Lesson 5: Gradient Descent Fundamentals 8min

Week 3: Building Your First Model

  • Lesson 1: Deep Dive into Gradient Descent 18min
  • Lesson 2: Classification Fundamentals 25min
  • Lesson 3: Neurons Explained 15min

Week 4: Training & Evaluation

  • Lesson 1: Practical Gradient Descent 20min
  • Lesson 2: Deep Dive into Feature Engineering 21min
  • Lesson 3: Feature Engineering Explained 8min
  • Lesson 4: Deep Dive into Overfitting 11min
  • Lesson 5: Working with Tensors 23min
  • Lesson 6: Deep Dive into Feature Engineering 22min

Week 5: Advanced Algorithms

  • Lesson 1: Advanced Tensors Techniques 17min
  • Lesson 2: Building Gradient Descent 21min
  • Lesson 3: Tensors Best Practices 17min
  • Lesson 4: Mastering Neurons 20min
  • Lesson 5: Working with Overfitting 22min
  • Lesson 6: Building Tensors 24min

Week 6: Neural Networks

  • Lesson 1: Hands-On Gradient Descent 8min
  • Lesson 2: Advanced Tensors Techniques 14min
  • Lesson 3: Practical Classification 21min
  • Lesson 4: Working with Overfitting 18min

Week 7: Model Deployment

  • Lesson 1: Deep Dive into Tensors 22min
  • Lesson 2: Mastering Tensors 17min
  • Lesson 3: Advanced Classification Techniques 10min
  • Lesson 4: Gradient Descent Best Practices 23min
  • Lesson 5: Practical Feature Engineering 12min

Week 8: Real-World Applications

  • Lesson 1: Introduction to Classification 24min
  • Lesson 2: Understanding Gradient Descent 19min
  • Lesson 3: Introduction to Gradient Descent 23min
Free
461 enrolled · 5.00 rating

Instructor: Zainab Yusuf

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