Get yourself trained on An Introduction to with this Online Training An Introduction to Machine Learning for Data Engineers.
Online Training An Introduction to Machine Learning for Data Engineers
Review from similar course:Another Excellent course from abrilliant Instructor. Really well explained, and precisely the right amount of information. Mike provides clear and concise explanations and has adeep subject knowledge ofGoogle’s Cloud.– Julie JohnsonWelcome toAn 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 DataEngineers.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, theGoogle Certified Data Engineeris the onlyreal world certificationfor data and machine learning engineers. Machine learningis 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, well 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. Youll 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 inthe 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 youre 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 your interest inAn Introduction to Machine Learning for Data Engineers.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.