Description

Joe Navarro’s Online Advanced Speed Reading People Course

Joe Navarro’s Online Advanced Speed Reading People Course
Category: Tutorial
English | Size: 2.08 GB
This course in nonverbal communications has been developed by the world-leading expert himself and is based on his 25-years in the FBI and his books, What Every BODY is Saying and Louder Than Words.
The course allows you to self-study at home, at your own pace, under Joe’s guidance and using Joe’s proprietary materials and techniques which he has used with the FBI. Your work will be personally evaluated by Joe and upon successful completion of the course students will receive a certificate of completion.

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.

 

In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it – by simply changing the pictures in the input folder.

For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog!