In this one-day advanced-level course, you will learn and deploy neural-network based Deep Learning AI to solve highly complex problems. You will also discover text data mining for leading-edge analytics of textual data. Finally we will investigate ensemble methods for creating winning data-science algorithms.
Throughout the day theory will be complemented by “peer-instruction”; a teaching method that improves your learning experience by asking you to solve examples. This will provide you with valuable experience that you can apply to your own problems.
Who will benefit
This course is aimed towards developers, in which we will delve into the mathematics behind the code as well as developing real life algorithms in Python. One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor.
This is an advanced-level course, and it is expected that you will have had some experience to Python and significant experience in Data Science and Machine Learning. This can be achieved by attending the intermediate course.
What you will achieve
The day will comprise of a series of sub-hour theoretical sessions separated by practical exercises. It will cover a range of topics, but it is expected that you will be able to:
- Develop solutions to mine, analyse and classify text
- Discuss and explain neural networks, deep learning and a range of topologies
- Employ semi-supervised machine learning to complex problems
- Use ensemble methods to create cutting-edge machine learning products
Topics covered in this training
- Text feature engineering
- Text mining, representation and learning
- Neural networks
- Deep belief networks
- Stacked denoising autoencoders
- Convolutional neural networks
- Semi-supervised machine learning
- Ensemble methods
- An in-depth practical example demonstrating the day’s concepts