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Ezra Firestone – Smart Email Marketing 2.0

Ezra Firestone – Smart Email Marketing 2.0 | 11.2 GB
Turn email into a huge revenue channel for your brand in 2019.(It should account for 30-40% of your total sales!)
Turn email into a huge revenue channel for your brand in 2019.
(It should account for 30-40% of your total sales!)

In Ezra’s 6-week email course, he shows you how to use advanced email marketing strategies to grow your ecommerce business in 2019.

Copy his most profitable campaigns for converting new

customers, creating repeat customers, running lucrative sales and more.

In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans.

The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course!

In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.

According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.

This is the first part of Volume 2 – Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.