SciKit-Learn in Python for Machine Learning Engineers

About SciKit-Learn in Python for Machine Learning Engineers

Instructor very knowledgeable about the material, and explains it clearly and to the point. Also, gives very good practical examples.

- Diana

As usual, Mike provides a well made course to teach you about SciKit. The lessons are very short so you are able to absorb the information, and the follow up labs help anchor what you learned. I will be going over this course again because the information is a bit advanced, but I already got a great understanding and feel for SciKit after my first go through of the course. It is recommended you do take the 3 previous courses before you start this one because they build on each other. Mike West is a top instructor on the subject of python and data and his courses are worth the time and spent.

- Joseph

So far, so good. The quick lectures throw out a lot of information, so I typically watch them again later. Good course thus far.

- Ted Welcome to

SciKit-Learn in Python for Machine Learning Engineers

This is the

fourth course

in the series designed to prepare you for a

real world job

in the

machine learning space.

I'd highly recommend you

take the courses serially

. People love building models and many think that machine learning engineers sit around and build models all day. They don't.

Take the courses

in order to

understand

what machine learning engineers

really do.

Thank you!!

In this course we are going to

learn

SciKit-Learn using a

lab integrated approach

. Programming is something you must

do

to master it. You

_can't read about Python_ and expect to learn it.

If you take this course

from start to finish

you'll

know

the

core foundations of a machine learning library in Python called SciKit-Learn

, you'll understand the very basics of

model building

and lastly, you'll apply what you’ve learned by building many traditional machine learning models in SciKit-Learn. This course is

centered around building

traditional machine learning models in

SciKit-Learn

This course is an

applied course on machine learning

. Here' are a few items you'll learn:

SciKit-Learn basics from A-Z

Lab integrated. Please don't just watch. Learning is an interactive event. Go over every lab in detail.

Real world Interviews Questions

Build a basic model build in SciKit-Learn. We call these traditional models to distinguish them from deep learning models.

Learn the vernacular of building machine learning models.

If you're new to programming or machine learning you might ask,

why would I want to learn SciKit-Learn

? Python has become the

gold standard

for

building machine learning models

in the applied space and SciKit-Learn has become the gold standard for building traditional models in Python. The term "applied" simply means the real world.

Machine learning

is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes

_without being explicitly programmed._

The key part of that definition is “without being explicitly programmed.” If you're interested in working as a

machine learning engineer, data engineer or data scientist

then you'll have to know

Python

. The good news is that _Python is a_

_high level language_

. That means it was designed with ease of learning in mind. It's very

user friendly

and has a lot of applications outside of the ones we are interested in. In

SciKit-Learn in Python for Machine Learning Engineers

we are going to

start with the basics

. You'll learn the basic terminology, how to score models and everything in between. As you

learn SciKit-Learn

you'll be

completing labs

that will build on what you've learned in the previous lesson so

please

_don't skip any._

Five Reasons to take this Course.

1) You Want to be a Machine Learning Engineer

It's one of the most sought-after careers in the world. The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. Without a solid understanding of Python, you'll have a hard time of securing a position as a machine learning engineer.

2) The Google Certified Data Engineer

Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone. Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google. You can't become a data engineer without learning Python.

3) The Growth of Data is Insane

Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month. _Almost all real-world machine learning is supervised_. That means you point your machine learning models at clean tabular data. We need clean data to build our SciKit-Learn models with.

4) Machine Learning in Plain English

Machine learning is one of the hottest careers on the planet and understanding the basics is required to attaining a job as a data engineer. Google expects data engineers and their machine learning engineers to be able to build machine learning models. In this course, you'll learn enough Python to be able to build a deep learning model.

5) You want to be ahead of the Curve

The data engineer and machine learning engineer roles are fairly new. While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field. You know that the first to be certified means the first to be hired and first to receive the top compensation package. Thanks for interest in

SciKit-Learn in Python for Machine Learning Engineers

See you in the course!!