Total Training is excited to announce the release of two new courses: Deep Learning & Computer Vision: An Introduction and Quant Trading Using Machine Learning.

These two online training courses offer you a chance to jump into the popular topic of Machine Learning. Covering concepts such as Python programming, Artificial Intelligence, and Data Analytics, these two courses provide nearly 13 hours of intensive instruction. Deep Learning & Computer Vision: An Introduction will allow you to grasp underlying theories, while Quant Trading Using Machine Learning will allow you to take your working knowledge of Python and apply it to more advanced Quant Trading models.

Deep Learning & Computer Vision Course Highlights:

• Discover Core Machine Learning Concepts & Build An Artificial Neural Network
• Design and Implement a simple computer vision use-case: digit recognition
• Confidently move on to more complex and comprehensive material on these topics
• Grasp the theory underlying deep learning and computer vision
• Understand use-cases for computer vision as well as deep learning

Deep Learning & Computer Vision Target Audience:

• Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
• Engineers who want to understand or learn machine learning and apply it to problems they are solving
• Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
• Tech executives and investors who are interested in big data, machine learning or natural language processing
• MBA graduates or business professionals who are looking to move to a heavily quantitative role

Deep Learning & Computer Vision Course Requirements:

•  No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory.
•  Working knowledge of Python would be helpful if you want to run the source code that is provided.

Quant Trading Course Highlights:

• Play the Markets Like a Pro by Integrating Machine Learning into Your Investment Strategies.
• Develop Quant Trading models using advanced Machine Learning techniques
• Compare and evaluate strategies using Sharpe Ratios
• Use techniques like Random Forests and K-Nearest Neighbors to develop Quant Trading models
• Use Gradient Boosted trees and tune them for high performance
• Use techniques like Feature engineering, parameter tuning and avoiding overfitting
• Build an end-to-end application from data collection and preparation to model selection

Quant Trading Target Audience:

• Quant traders who have not used Machine learning techniques before to develop trading strategies
• Analytics professionals, modelers, 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

Quant Trading Course Requirements:

• 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.

How can I get these courses?

• These courses are available as single title purchases:
  • Machine Learning – Deep Learning & Computer Vision: An Introduction
  • Machine Learning – Quant Trading

• These courses are available as part of the Total Training All-Access Library.
• If you are a current All-Access subscriber, these courses are automatically added to your Library!

Sample Video:

Stay Tuned for more Machine Learning & Artificial Intelligence course releases over the next several weeks!

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About the Presenters:

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

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