Machine Learning, Data Science and Deep Learning
Course Description
This package provides an understanding of the foundations of data science and its applications and a broad introduction to machine learning and algorithms and learn how the data science process can be used to address business problems. In this course you will also learn about the basics of deep neural networks, and their applications to various tasks. The course aim is to present the mathematical, statistical, and computational challenges of building stable representations for high-dimensional data.
Course Objectives:
| · | Design one or more services or applications within an organization |
| · | Understand the processes of the business well for creating a cohesive product vision |
| · | Responsible for building and integration of computer systems and information for meeting specific needs |
| · | Learn the tools required for building Deep Learning models |
| · | Explore multiple architectures and understand how to fine-tune and continuously improve models |
| · | Learn how the same task can be solved using multiple Deep Learning approaches |
| · | How machine learning uses algorithms to search for patterns in data |
| · | Learn the principles of machine learning and the importance of algorithms |
| · | Understanding of machine learning |
| · |
Learn how complex data can be harnessed to grow and scale an organization |
Course Contents:
| · | Introduction |
| · | Shallow and Deep Neural Networks |
| · | Explaining and Harnessing Adversarial Examples |
| · | Optimization |
| · | Gradient Checking |
| · | Hyperparameter tuning |
| · | Programming Frameworks |
| · | Conditional GAN, CycleGAN |
Find out more about Amfasoft's education offerings
