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Gerry Cramer & Mike Vestil – Passion To Profit

[Download] Gerry Cramer & Mike Vestil – Passion To Profit | 3.24 GB

Do This BEFORE The Web Class Starts!
1. BLOCK OUT 2.5 HOURS: Make sure that you block out a full 2.5 hours for the live training in your calendar. It’s a good idea to put a sticky note on your computer
2. SHOW UP EARLY: There will be no recordings so make sure that you attend live and show up at least 5-minutes early. The software we use has an attendee limit and with thousands of people, we’ll fill up fast.
3. USE A DESKTOP COMPUTER: Make sure that you attend the live training using your LAPTOP or DESKTOP computer. The webinar system we use doesn’t behave well with mobile so using a computer will dramatically improve your experience and allow you to learn more.
BONUS: Subscribe to the YouTube channel to learn more about passive income, wealth generation, and wealth preservation.

practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn’t even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.

Mastering Deep Learning is not just about knowing the intuition and tools,

it’s also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That’s why in this course we are introducing six exciting challenges:

In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank’s customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.

Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.

If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.