What You Will Learn :
- Build a solid knowledge base on data mining techniques and tools, as well as their application to the financial industry
- Gain hands-on experience with Natural Language Processing and Deep Learning in finance
- Learn how to apply Python to data mining and processing, and to solve real-world NLP and DL problems
- Gain an understanding of Artificial Neural Networks (ANN) algorithms and how to use them to design, build and develop DL models
This hands-on Machine Learning and AI Techniques programme covers key techniques – including several aspects of supervised and unsupervised machine learning – that can be used when mining financial data. The programme also focuses on advanced data science techniques that are becoming widely used in financial markets for text analysis and Artificial Intelligence (AI): Natural Language Processing (NLP) and Deep Learning (DL).
The programme is delivered entirely through workshops and case studies. Participants will learn how to implement natural language processing techniques by building a sentiment analysis model to analyze text. In the deep learning section, participants will focus on the different neural networks that can be put at work for data classification, time-series forecasting and pattern recognition.
All exercises and case studies are illustrated in Python, allowing you to learn how to work with this flexible, open-source programming language.
Basic programming experience in Python is recommended, which can be acquired in the 2-day LFS Python for Finance programme.
Can’t travel? Don’t want to travel? LFS Live brings the class to you!
- Live interactive training from world renowned practitioners in the comfort of your own home
- Real classroom experience without the inconvenience of travel
- World class teaching from the comfort of your preferred location
Who is this course for?
This course is primarily aimed at those working in financial institutions; as well as regulatory bodies, advisory firms and technology vendors. Specific job titles may include but are not limited to:
Quantitative analytics and modelling
Infrastructure and technology
Applicants should come to the course with basic knowledge of statistics and a good working knowledge of Excel and Python.