Description

Smithsonian Ch. – Flying High with Phil Keoghan (2017)

Smithsonian Ch. – Flying High with Phil Keoghan (2017)
Category: Tutorial
English | Size: 1.44 GB
Flying High with Phil Keoghan
World traveler and Amazing Race host Phil Keoghan invites you on a one-of-a-kind tour of his homeland, New Zealand. On this cross-country tour at the edge of the world, Keoghan visits eccentric and fascinating places and people who epitomize the spirit of Kiwi innovation. From “The Lord of the Rings” film director Peter Jackson to a revolutionary farmer who uses drones to herd sheep, Keoghan brings you captivating and humorous stories you just won’t find in a travel guide.

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!