Machine Learning – Quant Trading

Overview

This ’Quant Trading Using Machine Learning’ online training course takes a completely practical approach to applying Machine Learning techniques to Quant Trading. The focus is on practically applying ML techniques to develop sophisticated Quant Trading models. From setting up your own historical price database in MySQL, to writing hundreds of lines of Python code, the focus is on doing from the get-go. Supplemental Material included!

Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.

This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).

Who is it for?

• Quant traders who have not used Machine learning techniques before to develop trading strategies
• Analytics professionals, modellers, big data professionals who want to get hands-on experience with Machine Learning
• Anyone who is interested in Machine Learning and wants to learn through a practical, project-based approach

Course description:

This course consists of the following modules:

• Module 01: You, This Course & Us
• Module 02: Developing Trading Strategies in Excel
• Module 03: Setting up your Development Environment
• Module 04: Setting up a Price Database
• Module 05: Decision Trees, Ensemble Learning & Random Forests
• Module 06: A Trading Strategy as Machine Learning Classification
• Module 07: Feature Engineering
• Module 08: Engineering a Complex Feature – A Categorical Variable with Past Trends
• Module 09: Building a Machine Learning Classifier in Python
• Module 10: Nearest Neighbors Classifier
• Module 11: Gradient Boosted Trees
• Module 12: Introduction to Quant Trading

Course Duration:

From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.

Method of Assessment:

At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.

Certification:

Successful candidates will be awarded a certificate for Machine Learning – Quant Trading.

Entry Requirement:

• Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.
• Working knowledge of Python is necessary if you want to run the source code that is provided.
• Basic knowledge of machine learning, especially Machine Learning classification techniques, would be helpful but it’s not mandatory.

Career Path:

This course will provide you with the knowledge and skills to gain high level job roles in the following industries:

• Machine learning
• Quantitative research
• Risk analysis
• Quantitative trading

Presenter Information:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

PLEASE NOTE: We do not provide any software with this course.

Course Curriculum Free Introduction 1: You, This Course & Us 2: Developing Trading Strategies in Excel 3: Setting up your Development Environment 4: Setting up a Price Database 5: Decision Trees, Ensemble Learning & Random Forests 6: A Trading Strategy as Machine Learning Classification 7: Feature Engineering 8: Engineering a Complex Feature – A Categorical Variable with Past Trends 9: Building a Machine Learning Classifier in Python 10: Nearest Neighbors Classifier 11: Gradient Boosted Trees 12: Introduction to Quant Trading Course Reviews