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.
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.
Reasons to take this Course.*
1) You Want to be a Machine
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
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
3) The Growth of Data is
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
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
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.
for interest in SciKit-Learn in Python for Machine Learning Engineers
you in the course!!