In this course, you will learn the fundamentals of neural networks, including how to adjust for different variables, train a neural network with backpropagation, and prepare images and text data for modelling.
Through demonstrations and applied learning, you will explore how to build a complete neural network architecture to solve different types of problems as well as how to improve a neural network’s performance.
You will also learn about proper techniques and best practices in training and validating clustering models through unsupervised learning. Finally, you will work through the complete process of solving a clustering problem and learn how to evaluate and improve the performance of clustering algorithms.
Upon completion of this course, you will know how to:
- prepare data (image or text) for modelling
- build deep learning models to solve problems for an organization
- evaluate the performance of deep learning model
- build clustering models designed to solve problems for an organization
- evaluate the performance of unsupervised learning models.
PrerequisitesDATA 024 - Supervised Learning — Regression, Univariate, and Multivariate Time Series is a mandatory requirement for this course.
Applies Towards the Following Certificates
- Applied Machine Learning Certificate of Completion : Applied Machine Learning Certificate of Completion