Description

Master Life Coaching Certification #2 ECA Accreditation

Created by Empowerment Coach Academy, Louise Anne Maurice | Video: 1280×720 | Audio: AAC 48KHz 2ch | Duration: 06:25 H/M | Lec: 86 | 6.94 GB | Language: English | Sub: English [Auto-generated]
Master Life Coaching Globally Recognized Accreditation Program! Join Part 2 of 10 for Master Life Coaching Certification

What you’ll learn
August 2019 Course Updated to support your success Life Coaching.
Work as a Life Coach while learning with our structured practical assignments.
Coach the self-image issues of the top 6 profiles who hire Life Coaches.

Master simple NLP coaching techniques to move your clients past self-doubt.
Start a new Life Coach Career, build a business or become indispensable.
Enroll clients & effectively run a coaching session even if you have never coached.

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!

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.