What you will learn in Neural Networks and Deep Learning Course
- Understand the foundational concepts of neural networks and deep learning.
- Build, train, and apply fully connected deep neural networks.
- Implement efficient (vectorized) neural networks.
- Identify key parameters in a neural network’s architecture.
- Apply deep learning techniques to real-world applications.
Program Overview
Introduction to Deep Learning
⏱️2 hours
- Analyze the major trends driving the rise of deep learning.
- Understand where and how deep learning is applied today.
Neural Networks Basics
⏱️5 hours
- Learn the structure and functioning of neural networks.
- Implement forward and backward propagation.
Shallow Neural Networks
⏱️ 6 hours
- Build a shallow neural network and understand its components.
- Apply vectorization to optimize computations.
Deep Neural Networks
⏱️ 6 hours
- Construct deep neural networks with multiple layers.
- Understand the role of activation functions and parameters.
Get certificate
Job Outlook
- Proficiency in neural networks and deep learning is essential for roles such as Machine Learning Engineer, Data Scientist, and AI Researcher.
- Skills acquired in this course are applicable across various industries, including technology, healthcare, finance, and more.
- Completing this course can enhance your qualifications for positions that require expertise in deep learning and neural network architectures.
Specification: Neural Networks and Deep Learning
|
FAQs
- Neural networks are computer models inspired by the way the human brain works.
- They are used to recognize patterns, process data, and make predictions.
- Applications include image recognition, chatbots, voice assistants, and recommendation systems.
- Learning them gives you the foundation to understand and build AI-powered tools.
- You don’t need advanced math — just a basic grasp of algebra, calculus, and probability.
- Most concepts are explained with visuals and coding examples instead of heavy theory.
- Practice exercises help you understand the math step by step.
- The focus is more on applying ideas rather than solving complex equations.
- No strict requirement for a computer science degree.
- Suitable for students, professionals, and even career changers.
- Learners from engineering, business, healthcare, or social sciences can join.
- The course starts with basics and gradually moves to deeper concepts.
- Yes, you’ll build real neural networks during the course.
- Coding exercises help you apply concepts immediately.
- Projects use real-world datasets to practice problem-solving.
- By the end, you’ll have both theory and practical implementation experience.
- You’ll have a strong foundation in deep learning and AI.
- You can explore advanced areas like computer vision and natural language processing.
- Helps in careers such as Data Scientist, AI Engineer, or Machine Learning Specialist.
- It boosts your resume and prepares you for industry projects or further study.
