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Cat Howell – FB Ads Academy

Cat Howell – FB Ads Academy | 26.18 GB

How To Scale Your Business To $100K/mo Revenues
Running Digital Marketing Services For Clients
THE WORLD?S #1 AGENCY SYSTEM FOR ENTREPRENEURS, FREELANCERS, AND AGENCIES!
Unlock more time and revenue by plugging into the support, processes and systems of an actual seven figure agency!
What if instead of having a business that makes you feel stuck & overwhelmed, you could run a business that?
– Allows you the time to spend working ON your business, rather than IN your business
– Let?s you focus on your areas of core competence – the things you actually like, rather than the ones you have to
– Consistently hits your revenue targets
– Delivers EPIC, tangible results for your clients
– Allows you more freedom so you can spend time with friends & family
– Brings in quality, high-paying leads that stick around – no more poor-paying clients!
– Has all the necessary systems & process in place so things can run smoothly & effectively

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.

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!

 

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 m