Machine Learning for Banking (with Python) Training Course

Course CodeCourse Code

mlbankingpython_

Duration Duration

21 hours (usually 3 days including breaks)

Requirements Requirements

  • Experience with Python programming
  • Basic familiarity with statistics and linear algebra

Overview Overview

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Python will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

  • Developers
  • Data scientists

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Course OutlineCourse Outline

Introduction

  • Difference between statistical learning (statistical analysis) and machine learning
  • Adoption of machine learning technology and talent by finance and banking companies

Different Types of Machine Learning

  • Supervised learning vs unsupervised learning
  • Iteration and evaluation
  • Bias-variance trade-off
  • Combining supervised and unsupervised learning (semi-supervised learning)

Machine Learning Languages and Toolsets

  • Open source vs proprietary systems and software
  • Python vs R vs Matlab
  • Libraries and frameworks

Machine Learning Case Studies

  • Consumer data and big data
  • Assessing risk in consumer and business lending
  • Improving customer service through sentiment analysis
  • Detecting identity fraud, billing fraud and money laundering

Hands-on: Python for Machine Learning

  • Preparing the Development Environment
  • Obtaining Python machine learning libraries and packages
  • Working with scikit-learn and PyBrain

How to Load Machine Learning Data

  • Databases, data warehouses and streaming data
  • Distributed storage and processing with Hadoop and Spark
  • Exported data and Excel

Modeling Business Decisions with Supervised Learning

  • Classifying your data (classification)
  • Using regression analysis to predict outcome
  • Choosing from available machine learning algorithms
  • Understanding decision tree algorithms
  • Understanding random forest algorithms
  • Model evaluation
  • Exercise

Regression Analysis

  • Linear regression
  • Generalizations and Nonlinearity
  • Exercise

Classification

  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercise

Hands-on: Building an Estimation Model

  • Assessing lending risk based on customer type and history

Evaluating the performance of Machine Learning Algorithms

  • Cross-validation and resampling
  • Bootstrap aggregation (bagging)
  • Exercise

Modeling Business Decisions with Unsupervised Learning

  • When sample data sets are not available
  • K-means clustering
  • Challenges of unsupervised learning
  • Beyond K-means
  • Bayes networks and Markov Hidden Models
  • Exercise

Hands-on: Building a Recommendation System

  • Analyzing past customer behavior to improve new service offerings

Extending your company's capabilities

  • Developing models in the cloud
  • Accelerating machine learning with GPU
  • Applying Deep Learning neural networks for computer vision, voice recognition and text analysis

Closing Remarks

Bookings, Prices and EnquiriesBookings, Prices and Enquiries

Guaranteed to run even with a single delegate!
Private Classroom
 
Private Classroom
Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private Remote
From £3300
Private Remote
The instructor and the participants are in two different physical locations and communicate via the Internet. More Information

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Private Remote
1 £3300
2 £2325
3 £2000
4 £1838
Public Classroom
From £3750
(88)
Public Classroom
Participants from multiple organisations. Topics usually cannot be customised

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Public Classroom
1 £3750
2 £2575
3 £2183
4 £1988
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Related Courses

Upcoming Courses

VenueCourse DateCourse Price [Remote / Classroom]
Leicester - St. Georges HouseMon, 2018-02-05 09:30£3300 / £4050
Edinburgh Training and Conference VenueMon, 2018-02-05 09:30£3300 / £3525
London, Hatton GardenTue, 2018-02-06 09:30£3300 / £4425
OxfordWed, 2018-02-07 09:30£3300 / £4125
Coventry - The QuadrantMon, 2018-02-12 09:30£3300 / £4050

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