Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Machine Learning for Finance

  • Course Code: Data Science - Machine Learning for Finance
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to get a guide to advances in machine learning for financial professionals, with working Python code

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning for Finance skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to get a guide to advances in machine learning for financial professionals, with working Python code 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Machine Learning for Finance expert instructor, students will learn about and explore: 

  • Explore advances in machine learning and how to put them to work in financial industries 
  • Gain expert insights into how machine learning works, with an emphasis on financial applications 
  • Discover advanced machine learning approaches, including neural networks, GANs, and reinforcement learning 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Apply machine learning to structured data, natural language, photographs, and written text 
  • Understand how machine learning can help you detect fraud, forecast financial trends, analyze customer sentiments, and more 
  • Implement heuristic baselines, time series, generative models, and reinforcement learning in Python, scikit-learn, Keras, and TensorFlow 
  • Delve into neural networks, and examine the uses of GANs and reinforcement learning 
  • Debug machine learning applications and prepare them for launch 
  • Address bias and privacy concerns in machine learning 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to get a guide to advances in machine learning for financial professionals, with working Python code 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Neural Networks and Gradient-Based Optimization 
  • Neural Networks and Gradient-Based Optimization 
  • 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 
  1. Applying Machine Learning to Structured Data 
  • Applying Machine Learning to Structured Data 
  • 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 
  1. Utilizing Computer Vision 
  • Utilizing Computer Vision 
  • 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 
  1. Understanding Time Series 
  • Understanding Time Series 
  • Visualization and preparation in pandas 
  • Fast Fourier transformations 
  • Autocorrelation 
  • Establishing a training and testing regime 
  • A note on backtesting 
  • Median forecasting 
  • ARIMA 
  • Kalman filters 
  • Forecasting with neural networks 
  • Conv1D 
  • Dilated and causal convolution 
  • Simple RNN 
  • LSTM 
  • Recurrent dropout 
  • Bayesian deep learning 
  1. Parsing Textual Data with Natural Language Processing 
  • Parsing Textual Data with Natural Language Processing 
  • 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 
  1. Using Generative Models 
  • Using Generative 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 
  1. Reinforcement Learning for Financial Markets 
  • Reinforcement Learning for Financial Markets 
  • 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 
  1. Privacy, Debugging, and Launching Your Products 
  • Privacy, Debugging, and Launching Your Products 
  • Debugging data 
  • Debugging your model 
  • Deployment 
  1. Fighting Bias 
  • Fighting Bias 
  • 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 
  1. Bayesian Inference and Probabilistic Programming 
  • Bayesian Inference and Probabilistic Programming 
  • An intuitive guide to Bayesian inference 
View All Courses

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?