en_Welcome to ML with Python.txt

Hello, and welcome to Machine Learning with Python.
In this course, you’ll learn how Machine Learning is used in many key fields and industries.
For example, in the health care industry, data scientists use Machine Learning to predict
whether a human cell that is believed to be at risk of developing cancer, is either benign
or malignant.
As such, Machine learning can play a key role in determining a person’s health and welfare.
You’ll also learn about the value of decision trees and how building a good decision tree
from historical data helps doctors to prescribe the proper medicine for each of their patients.
You’ll learn how bankers use machine learning to make decisions on whether to approve loan
applications.
And you will learn how to use machine learning to do bank customer segmentation, where it
is not usually easy to run for huge volumes of varied data.
In this course, you’ll see how machine learning helps websites such as YouTube, Amazon, or
Netflix develop recommendations to their customers about various products or services, such as
which movies they might be interested in going to see or which books to buy.
There is so much that you can do with Machine Learning!
Here, you’ll learn how to use popular python libraries to build your model.
For example, given an automobile dataset, we use the sci-kit learn (sklearn) library
to estimate the Co2 emission of cars using their Engine size or Cylinders.
We can even predict what the Co2 emissions will be for a car that hasn’t even been
produced yet!
And we’ll see how the telecommunications industry can predict customer churn.
You can run and practice the code of all these samples using the built-in lab environment
in this course.
You don’t have to install anything to your computer or do anything on the cloud.
All you have to do is click a button to start the lab environment in your browser.
The code for the samples is already written using python language, in Jupyter notebooks,
and you can run it to see the results, or change it to understand the algorithms better.
So, what will you be able to achieve by taking this course?
Well, by putting in just a few hours a week over the next few weeks, you’ll get new
skills to add to your resume, such as regression, classification, clustering, sci-kit learn
and SciPy.
You’ll also get new projects that you can add to your portfolio, including cancer detection,
predicting economic trends, predicting customer churn, recommendation engines, and many more.
You’ll also get a certificate in machine learning to prove your competency, and share
it anywhere you like online or offline, such as LinkedIn profiles and social media.
So let’s get started.