An Introduction to Machine Learning for Data Engineers

Review from similar course: 

Another Excellent course from a brilliant Instructor. Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has a deep subject knowledge of Google’s Cloud.

 — Julie Johnson 

Welcome to An Introduction to Machine Learning for Data Engineers. This course is part of my series for data engineering. The course is a prerequisite for my course titled Tensorflow on the Google Cloud Platform for Data Engineers.

This course will show you the basics of machine learning for data engineers. The course is geared towards answering questions for the Google Certified Data Engineering exam.

This is NOT a general course or introduction to machine learning. This is a very focused course for learning the concepts you’ll need to know to pass the Google Certified Data Engineering Exam. 

At this juncture, the Google Certified Data Engineer is the only real world certification for data and machine learning engineers.

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.”

The vast majority of applied machine learning is supervised machine learning. The word applied means you build models in the real world. Supervised machine learning is a type of machine learning that involves building models from data that exists.

A good way to think about supervised machine learning is:  If you can get your data into a tabular format, like that of an excel spreadsheet, then most machine learning models can model it.

In the course, we’ll learn the different types of algorithms used. We will also cover the nomenclature specific to machine learning. Every discipline has their own vernacular and data science is not different.

You’ll also learn why the Python programming language has emerged as the gold standard for building real world machine learning models.

Additionally, we will write a simple neural network and walk through the process and the code step by step. Understanding the code won’t be as important as understanding the importance and effectiveness of one simple artificial neuron.

*Five Reasons to take this Course.*

1) You Want to be a Data Engineer 

It’s the number one job in the world. (not just within the computer space) 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.

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.

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.

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 to be able to build machine learning models. In this course, we will cover all the basics of machine learning at a very high level.

5) You want to be ahead of the Curve 

The data engineer role is 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 your interest in  An Introduction to Machine Learning for Data Engineers.