Course description
Machine Learning and AI Techniques
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 Machine Learning and AI Techniques 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.
Upcoming start dates
Suitability - Who should attend?
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:
- Trading
- Portfolio management
- Asset allocation
- Data science
- Financial engineering
- 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.
Outcome / Qualification etc.
Learning Objectives
- 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 course is eligible for CE/CPD credit hours from CFA and GARP Institutes.
Training Course Content
Day One
Positioning of Machine Learning vs. Deep Learning Machine Learning Introduction
- Supervised vs. unsupervised
- Association rules
- Classification vs. regression problems
- Cross validation and hyper parameter optimization
Unsupervised Learning
- Clustering analysis
Workshop: Equity / credit models
- Outlier detection
- Distance Metrics in Sklearn
Workshop: Robust outlier detection
- Kernel Density Estimation
Workshop: BitCoin-application
- Hidden Markov Models
Workshop: GBPEUR-timeseries analysis
Supervised Learning
- Regression with regularization
- Ridge regression
- Lasso
- Elastic Net
Workshop: Portfolio hedging
- Miscellaneous Regression Techniques
- Gaussian Process Regression (GPR)
- Principal Component Regression (PCR)
- Partial Least Squares (PLS)
Workshop: Volsurface smoothing
- Classification
- Naive Bayes classification: A straightforward and powerful technique to classify data
- Linear Discriminant Analysis (LDA)
- Logistic Regression
Workshop: Classification trees
Day Two
Natural Language Processing
- Extracting real value from social media posts, images, email, PDFs and other sources of unstructured data is a big challenge for enterprises
- Explore and tokenize a text
- Sentiment analysis
- Text Classification
- Understanding concepts such as WordNet, Word2Vec, Stemming, etc.
Workshop: Sentiment analysis of tweets
Deep Learning (AI)
- Deep Learning as a subfield of machine learning - Artificial Neural Networks (ANN) algorithms
- Forward and backward propagation
- Network topology
- Tensorflow 2.0
Workshop: Regression, classification and time series forecast
Course delivery details
Courses are delivered in the London classroom and live online via LFS Live in London, New York, and Singapore time zones.
Please contact LFS for more details.
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