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 toSciKit-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 coursesin order to understand what machine learning engineersreally do.

Thank you!!

In this course we are going tolearnSciKit-Learn using alab integrated approach. Programming is something you mustdoto master it. Youcan't read about Pythonand expect to learnit.

If you take this coursefrom start to finish you'llknowthecore foundations of a machine learning library in Python called SciKit-Learn, you'll understand the very basics ofmodel building and lastly, you'll apply what you’ve learned by building many traditional machine learning models in SciKit-Learn.

This course is centered aroundbuilding traditional machine learning models in SciKit-Learn

This course is anapplied course on machine learning. Here' are a few itemsyou'll learn:

• SciKit-Learn basics from A-Z

• Lab integrated. Please don't justwatch. 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 themfrom 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 thegold standardforbuilding machine learning modelsin 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 learningis a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomeswithout being explicitly programmed.The key part of that definition is “without being explicitly programmed.”

If you're interested in working as amachine learning engineer,data engineeror data scientistthen you'll have to knowPython. The good news is thatPython is ahigh level language. That means it was designed with ease of learning in mind. It's veryuser friendlyand has a lot of applications outside of the ones we are interested in.

InSciKit-Learn in Python for Machine Learning Engineers we are going tostart with the basics. You'll learn the basic terminology, how to score models and everything in between.

As youlearn SciKit-Learn you'll becompleting labsthat will build on what you've learned in the previous lesson sopleasedon'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 ourSciKit-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 engineersto 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 rolesarefairly new. While you’re learning, building your skills andbecoming certified you arealso the first to be part of this burgeoning field. You know that the first to be certified meansthe first to be hired and first to receive the top compensation package.

Thanks for interest inSciKit-Learn in Python for Machine Learning Engineers

See you in the course!!