Get yourself trained on SciKit-Learn in Python with this Online Training SciKit-Learn in Python for Machine Learning Engineers.
Online Training SciKit-Learn in Python for Machine Learning Engineers
Welcome toSciKit-Learn in Python for Machine Learning EngineersThis 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 youve 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. *FiveReasons to take this Course.*1) You Want to be a MachineLearning EngineerIt’sone of the most sought-after careers in the world. The growth potential careerwise is second to none. You want the freedom to move anywhere you’d like. Youwant 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’llhave a hard time of securing a position as a machine learning engineer.2)The Google Certified DataEngineerGoogleis always ahead of the game. If you were to look back at a timeline of theiraccomplishments 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 theonly cloud vendor to have a data engineering certification. With their trackrecord I’ll go with Google. You can’t become a data engineer withoutlearning Python.3)The Growth of Data isInsaneNinetypercent of all the world’s data has been created in the last two years.Business around the world generate approximately 450 billion transactions aday. The amount of data collected by all organizations is approximately 2.5exabytes a day. That number doubles every month. Almost all real-worldmachine learning is supervised. That means you point your machine learningmodels at clean tabular data. We need clean data to build ourSciKit-Learn models with.4) Machine Learning in PlainEnglishMachinelearning is one of the hottest careers on the planet and understanding thebasics is required to attaining a job as a data engineer. Google expectsdata engineers and their machine learning engineersto be able to buildmachine learning models. In this course, you’ll learn enough Python to be ableto build a deep learning model.5) You want to be ahead of theCurveThedata engineer and machine learning engineer rolesarefairly new.While youre learning, building your skills andbecoming certifiedyou arealso the first to be part of this burgeoning field. You knowthat the first to be certified meansthe first to be hired and first toreceive the top compensation package.Thanksfor interest inSciKit-Learn in Python for Machine Learning EngineersSeeyou in the course!!
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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.