Chris Winters – F.A.M. Facebook Agency Machine
Chris Winters – F.A.M. Facebook Agency Machine | 10.9 GB
Welcome to Facebook Agency Machine (FAM) by Chris Winters (Kallzu)
Help You Select A Profitable Niche
– Not every offline niche works for FB ads.
– Dont make the mistake of selecting the WRONG niche.
– We will give you a list of niches to select from and/or you can bring niche ideas to use for approval.
– Start knowing that your selected niche will work for your new FB Agency.
As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you’re done with Deep Learning A-Z™ your skills are on the cutting edge of today’s technology.
then you will find this course extremely useful. Deep Learning A-Z™ is structured around special
coding blueprint approaches meaning that you won’t get bogged down in unnecessary
programming or mathematical complexities and instead you will be applying Deep Learning
techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
you will find this course refreshing, inspiring and very 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.
