Certificate in Deep Learning with Python

Deep Learning with Python

About Deep Learning with Python

Python is the ideal choice for machine learning and AI-based applications because of its flexibility, platform neutrality, availability of excellent libraries and frameworks for AI and machine learning (ML), simplicity, and consistency. These increase the language's general appeal.

Why is Deep Learning with Python important?

Python provides the stability, versatility, and wide range of tools needed for a machine learning or artificial intelligence project. From the phases of creation through deployment and up until the maintenance stage, Python allows developers to be productive and confident in the product that they are manufacturing.

It's simple to comprehend Python, and once you do, you may utilize those abilities to launch a fantastic career in the quickly growing data science sector. Even better, as more and more machine learning applications are developed daily, there will be a high need for Python programmers, which will benefit your career.

Who should take the Deep Learning with Python Exam?

•  
• Programmers
• Professional mathematicians willing to learn how to analyze data programmatically
• Python Developers
• AI/ML Developers

Deep Learning with Python Certification Course Outline

1. Overview of Deep Learning
2. Why is Deep Learning required?
3. Concept of ANN
4. Anatomy and function of neurons
5. The architecture of a neural network
6. Single-layer perceptron (SLP) model
7. Radial Basis Network (RBN)
8. Multi-layer perceptron (MLP) Neural Network
9. Recurrent neural network (RNN)
10. Long Short-Term Memory (LSTM) networks
11. Boltzmann Machine Neural Network
12. What is the Activation Function?
13. Rectified Linear Unit (ReLU) function
14. What is Stochastic Gradient Decent?
15. Advantages and disadvantages of Neural Networks
16. Applications of Neural Networks
17. Exploring the dataset
18. Building the Artificial Neural Network
19. Compiling the artificial neural network
20. Components of convolutional neural networks
21. Building the CNN model