Finance with Machine Learning

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

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Hours :

Hands on Labs

Requirements

  • 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
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    mathematics or logic skills • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su
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Description

Finance with Machine Learning 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 Applied learning environment, led by our Python 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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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.