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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.

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Unlimited Duration

Last Updated

January 20, 2021

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Certification

Course Description

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.

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Course Curriculum

    • Neural Networks and Gradient-Based Optimization 00:00:00
    • Our journey in this course 00:00:00
    • What is machine learning? 00:00:00
    • Supervised learning 00:00:00
    • Unsupervised learning 00:00:00
    • Reinforcement learning 00:00:00
    • Setting up your workspace 00:00:00
    • Using Kaggle kernels 00:00:00
    • Using the AWS deep learning AMI 00:00:00
    • Approximating functions 00:00:00
    • A forward pass 00:00:00
    • A logistic regressor 00:00:00
    • Optimizing model parameters 00:00:00
    • Measuring model loss 00:00:00
    • A deeper network 00:00:00
    • A brief introduction to Keras 00:00:00
    • Tensors and the computational graph 00:00:00
    • Applying Machine Learning to Structured Data 00:00:00
    • The data 00:00:00
    • Heuristic, feature-based, and E2E models 00:00:00
    • The machine learning software stack 00:00:00
    • The heuristic approach 00:00:00
    • The feature engineering approach 00:00:00
    • Preparing the data for the Keras library 00:00:00
    • Creating predictive models with Keras 00:00:00
    • A brief primer on tree-based methods 00:00:00
    • E2E modeling 00:00:00
    • Utilizing Computer Vision 00:00:00
    • Convolutional Neural Networks 00:00:00
    • Filters on color images 00:00:00
    • The building blocks of ConvNets in Keras 00:00:00
    • More bells and whistles for our neural network 00:00:00
    • Working with big image datasets 00:00:00
    • Working with pretrained models 00:00:00
    • The modularity tradeoff 00:00:00
    • Computer vision beyond classification 00:00:00
    • Understanding Time Series 00:00:00
    • Visualization and preparation in pandas 00:00:00
    • Fast Fourier transformations 00:00:00
    • Autocorrelation 00:00:00
    • Establishing a training and testing regime 00:00:00
    • A note on backtesting 00:00:00
    • Median forecasting 00:00:00
    • ARIMA 00:00:00
    • Kalman filters 00:00:00
    • Forecasting with neural networks 00:00:00
    • Conv1D 00:00:00
    • Dilated and causal convolution 00:00:00
    • Simple RNN 00:00:00
    • LSTM 00:00:00
    • Recurrent dropout 00:00:00
    • Bayesian deep learning 00:00:00
    • Parsing Textual Data with Natural Language Processing 00:00:00
    • An introductory guide to spaCy 00:00:00
    • Named entity recognition 00:00:00
    • Part-of-speech (POS) tagging 00:00:00
    • Rule-based matching 00:00:00
    • Regular expressions 00:00:00
    • A text classification task 00:00:00
    • Preparing the data 00:00:00
    • Bag-of-words 00:00:00
    • Topic modeling 00:00:00
    • Word embeddings 00:00:00
    • Document similarity with word embeddings 00:00:00
    • A quick tour of the Keras functional API 00:00:00
    • Attention 00:00:00
    • Seq2seq models 00:00:00
    • Using Generative Models 00:00:00
    • Understanding autoencoders 00:00:00
    • Visualizing latent spaces with t-SNE 00:00:00
    • Variational autoencoders 00:00:00
    • VAEs for time series 00:00:00
    • GANs 00:00:00
    • Using less data – active learning 00:00:00
    • SGANs for fraud detection 00:00:00
    • Reinforcement Learning for Financial Markets 00:00:00
    • Catch – a quick guide to reinforcement learning 00:00:00
    • Markov processes and the bellman equation – A more formal introduction to RL 00:00:00
    • Advantage actor-critic models 00:00:00
    • Evolutionary strategies and genetic algorithms 00:00:00
    • Practical tips for RL engineering 00:00:00
    • Frontiers of RL 00:00:00
    • Privacy, Debugging, and Launching Your Products 00:00:00
    • Debugging data 00:00:00
    • Debugging your model 00:00:00
    • Deployment 00:00:00
    • Fighting Bias 00:00:00
    • Sources of unfairness in machine learning 00:00:00
    • Legal perspectives 00:00:00
    • Observational fairness 00:00:00
    • Causal learning 00:00:00
    • Interpreting models to ensure fairness 00:00:00
    • Unfairness as complex system failure 00:00:00
    • A checklist for developing fair models 00:00:00
    • Bayesian Inference and Probabilistic Programming 00:00:00
    • An intuitive guide to Bayesian inference 00:00:00

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