Machine Learning and AI Techniques

London Financial Studies
Course summary
Professional Training
2 days
3,190 GBP, 4,020 USD excl. VAT
Full time
Professional Training
Course Dates
Online courses
3,190 GBP

3,190 GBP
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New York
4,020 USD
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4,020 USD
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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.

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

Please contact us for more information.

    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

    • 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

    This course is also available in New York Time Zone and Singapore Time Zone

    Why choose London Financial Studies

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    About provider

    Exclusive Teaching for Capital Markets & Investment Bankers in Europe, Americas and Asia Pacific

    London Financial Studies are specialists in delivering professional development for finance professionals focusing on capital markets. LFS provide individuals, teams and companies with expert teaching that combines theoretical understanding with practical experience, giving them the knowledge to operate at the...

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    Contact info

    London Financial Studies

    34 Curlew Street
    SE1 2ND London

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    Average rating 4.9

    Based on 14 reviews.

    Quantative RIsk Analyst
    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
    Excellent introduction to machine learning and AI, as well as Python. I learnt more than I ever thought would be possible in three days!
    Officer III,Accounting & Reconciliation
    Very powerful course, will keep you up-to-date with Applied Machine Learning & AI techniques.
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