Professional Training
4.9 (15 Reviews)

Machine Learning and AI Techniques

London Financial Studies, In London (+3 locations)
2 days
3,670 GBP excl. VAT
Next course start
21 November, 2024 (+4 start dates)
Course delivery
Classroom, Virtual Classroom
2 days
3,670 GBP excl. VAT
Next course start
21 November, 2024 (+4 start dates)
Course delivery
Classroom, Virtual Classroom
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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 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

    Choose between 4 start dates

    21 November, 2024

    • Classroom
    • London
    • English

    21 November, 2024

    • Virtual Classroom
    • Online
    • English

    Enquire for more information

    • Classroom
    • New York
    • English

    Enquire for more information

    • Classroom
    • Singapore
    • English

    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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    Average rating 4.9

    Based on 15 reviews.
    Reviews are published according to our review policy.
    Write a review!
    Sabiu adam
    06 Jan 2024

    Business center

    Quantative RIsk Analyst
    18 Jul 2019
    Great Course!

    This is a great course! In three days I learned the most important parts of machine learning and AI. It will greatly help me to do my job better.

    Senior Analyst
    16 Apr 2019
    Excellent Introduction

    Excellent introduction to machine learning and AI, as well as Python. I learnt more than I ever thought would be possible in three days!

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    34 Curlew Street
    SE1 2ND London

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