Deep Learning Online Course
Unlock the power of deep learning and take your machine learning skills to the next level with this hands-on, comprehensive course. You’ll build a solid foundation in core deep learning concepts, including artificial neural networks, activation and loss functions, bias, and data handling techniques.
You’ll start with a primer in Python tailored for data science, covering critical tools like NumPy and Pandas for data manipulation, and Matplotlib for visualization. You'll also gain essential skills in data cleaning and preparation.
The course then dives into deep learning, introducing key models such as the MP Neuron, Perceptron, and Sigmoid Neuron, and exploring theoretical underpinnings like the Universal Approximation Theorem.
With hands-on projects in TensorFlow 2.x, you’ll learn how to build, train, evaluate, and optimize deep neural networks for real-world applications. By the end of the course, you’ll be well-prepared to tackle more advanced challenges and continue your journey toward deep learning mastery.
Who should take this Course?
This course is ideal for beginners who are eager to step into the world of deep learning. No prior experience in programming or machine learning is required. Whether you're a student, professional, or simply curious about AI, this course provides the skills and knowledge to get started with confidence.
Course Curriculum
- Introduction
- Getting the Basics Right
- Python Crash Course on Basics
- Python for Data Science - Crash Course
- MP Neuron Model
- MP Neuron in Python
- Summary of MP Neuron
- Perceptron
- Perceptron in Python
- Sigmoid Neuron
- Sigmoid Neuron Implement with Python
- Basic Probability
- Deep Neural Networks
- Universal Approximation Theorem
- Deep Learning with TensorFlow 2.x
- Activation Functions in Deep Learning Neural Networks
- Applying Deep Learning