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Course Skill Level:

Foundational to Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and basic IT & Linux skills

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others with Python skills who want a guide to advances in machine learning for financial professionals.
  • Skill-level: Foundation-level Machine Learning for Finance skills for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.

About this course

Machine Learning for Finance explores new advances in machine learning and shows how they can be applied across the financial sector, including insurance, transactions, and lending. This course explains the concepts and algorithms behind the main machine learning techniques and provides example Python code for implementing the models yourself. The course is based on Jannes Klaas experience of running machine learning training courses for financial professionals. Rather than providing ready-made financial algorithms, the course focuses on advanced machine learning concepts and ideas that can be applied in a wide variety of ways. The course systematically explains how machine learning works on structured data, text, images, and time series. You’ll cover generative adversarial learning, reinforcement learning, debugging, and launching machine learning products. Later lessons will discuss how to fight bias in machine learning. The course ends with an exploration of Bayesian inference and probabilistic programming.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning for Finance expert instructor, students will learn about and explore:
  • Advances in machine learning and how to put them to work in financial industries
  • Gaining expert insights into how machine learning works, with an emphasis on financial applications
  • Discovering advanced machine learning approaches, including neural networks, GANs, and reinforcement learning
  • Applying machine learning to structured data, natural language, photographs, and written text
  • Understanding how machine learning can help you detect fraud, forecast financial trends, analyze customer sentiments, and more
  • Implementing heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow
  • Delving into neural networks, and examining the uses of GANs and reinforcement learning
  • Debugging machine learning applications and preparing them for launch
  • Addressing bias and privacy concerns in machine learning

Course breakdown / modules

  • Our journey in this course
  • What is machine learning?
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Setting up your workspace
  • Using Kaggle kernels
  • Using the AWS deep learning AMI
  • Approximating functions
  • A forward pass
  • A logistic regressor
  • Optimizing model parameters
  • Measuring model loss
  • A deeper network
  • A brief introduction to Keras
  • Tensors and the computational graph

  • The data
  • Heuristic, feature-based, and E2E models
  • The machine learning software stack
  • The heuristic approach
  • The feature engineering approach
  • Preparing the data for the Keras library
  • Creating predictive models with Keras
  • A brief primer on tree-based methods
  • E2E modeling

  • Convolutional Neural Networks
  • Filters on color images
  • The building blocks of ConvNets in Keras
  • More bells and whistles for our neural network
  • Working with big image datasets
  • Working with pretrained models
  • The modularity tradeoff
  • Computer vision beyond classification

  • Visualization and preparation in pandas
  • Fast Fourier transformations
  • Autocorrelation
  • Establishing a training and testing regime
  • A note on backtesting
  • Median forecasting
  • Kalman filters
  • Forecasting with neural networks
  • Conv1D
  • Dilated and causal convolution
  • Simple RNN
  • LSTM
  • Recurrent dropout
  • Bayesian deep learning

  • An introductory guide to spaCy
  • Named entity recognition
  • Part-of-speech (POS) tagging
  • Rule-based matching
  • Regular expressions
  • A text classification task
  • Preparing the data
  • Bag-of-words
  • Topic modeling
  • Word embeddings
  • Document similarity with word embeddings
  • A quick tour of the Keras functional API
  • Attention
  • Seq2seq models

  • Understanding autoencoders
  • Visualizing latent spaces with t-SNE
  • Variational autoencoders
  • VAEs for time series
  • GANs
  • Using less data – active learning
  • SGANs for fraud detection

  • Catch – a quick guide to reinforcement learning
  • Markov processes and the bellman equation – A more formal introduction to RL
  • Advantage actor-critic models
  • Evolutionary strategies and genetic algorithms
  • Practical tips for RL engineering
  • Frontiers of RL

  • Debugging data
  • Debugging your model
  • Deployment

  • Sources of unfairness in machine learning
  • Legal perspectives
  • Observational fairness
  • Training to be fair
  • Causal learning
  • Interpreting models to ensure fairness
  • Unfairness as complex system failure
  • A checklist for developing fair models

  • An intuitive guide to Bayesian inference