Ready for the next steps?

Deep Learning for
Predictive Analytics

Practical course on how to build production-ready Deep Learning applications set in the field of Predictive Analytics.
Study the theory of Deep Learning and techniques of Predictive Analytics
Work on 4 real-life projects
Experience the typical Deep Learning/Data Science project life-cycle
Receive immediate help, feedback and code-reviews from the instructor
Feel ready and confident to apply your newly gained knowledge afterwards in the real-world

Get notified! 🎉

Get notified when the Deep Learning for Predictive Analytics course is ready in spring of 2019 and secure your 50% discount on release.

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Course Overview

7h+ video and PDF content prepare you to work on up to 4 real-life projects. Experience these projects within an unique course workflow:

What You Will Learn 🎓

Everything from data cleaning and preprocessing to the deployment of the trained models into production.

Feedforward Neural Networks

Theory and mathematics behind the basic neural network architecture that lies the foundation for all deep learning applications.

TensorFlow

Basic and advanced usage of Google’s Deep Learning library, with emphasis on efficiency and performance.

Data Preprocessing

Learn the necessary data-preprocessing steps like exploration, cleaning, scaling and transformation to make the raw, real-life data sets ready for training.

Efficient Data Input Pipelines

Dealing with large data sets result in a longer training time – even with powerful hardware. Implementation of efficient input pipelines for the input data on the software side may significantly lower the time for the training.

Advanced Training of Deep Learning Models

Learn about the theory and practical implementation of more sophisticated training methods that lower the training time and increase the performance of your Deep Learning models.

Performance Evaluation

Accuracy, recall, specificity, precision, fall-out, miss-rate, etc. – learn how to measure the performance of your models correctly and to interpret the result.

Training in a Web-Cloud

Train the models that you have implemented in a web cloud (Amazon Web Services)

Deployment into Production

Use TensorFlow Serving API, Docker and Kubernetes to deploy the models into a web cloud to make them accessible for your customers – scalable and in real time.

Real-Life Projects 🌎

Apply your newly gained knowledge by building Deep Learning models using real-world data sets and experience a typical workflow, from data cleaning and preprocessing to deployment of the trained models into production.
01

Customer Behavior Modeling

Build a Deep Leaning model to predict the outcome of a banking institution marketing campaign on whether the customer will subscribe a term deposit.

02

Demand-Supply Prediction

Forecast the volume of drivers and riders at a certain time period in a specific geographic area which max­imizes the utilization of drivers and ensures that riders can always get a car whenever and wherever they may need a ride.

03

Anomaly Detection Model

Implement a Deep Learning model that identifies fraudulent financial transactions.

04

Recommender System

Build a recommender system that provides personalized recommend­ations of an item X for the user Y.

Used Technologies

Additional Features

And That‘s Not Everything 🚀

Slack Group

Get access to the exclusive Slack group, stay connected with other students, exchange, get help, create together.

Personal Support

Ask questions and get personal support from the instructor during implementation of your projects.

Student Pairing

Optionally, pair up with a fellow student to tackle the course together in order to keep up motivation and work flow.

Limited Places

Classes are limited to 25 students to guarantee the best personal support from the instructor.

Lifelong Access

Lifelong access to all video content and the Slack group.

30-Days Money Back

If you are not satisfied with the experience you can receive a refund within the first 30 days – no questions asked.

Artem Oppermann
Your Instructor

Artem Oppermann

MSc. Physics | Deep Learning & AI Software Developer | Expertise in Predictive Analytics, Reinforcement Learning, Computer Vision and Natural Language Processing

MediumGitHub

When I began to study Deep Learning I took many excellent online courses on several platforms like Udemy, Udacity and Coursera. Although they provided me with great knowledge about the subject, all courses lacked one particular last step I was looking for: How to apply my knowledge in a real production environment.

I was confident in building Deep Learning models locally, but facing the real world and do the same for customers or employers is a whole different story. In the end, I acquired the necessary skills the hard way by fighting through countless bite-sized pieces of information.

I want to provide a shortcut in learning for those who want to work in this amazing field professionally.

Secure Your 50% Discount 💰

Get notified when Deep Learning for Predictive Analytics is ready in spring of 2019 and to secure your 50% discount on release.

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