Get yourself trained on Data Wrangling in with this Online Training Data Wrangling in Pandas for Machine Learning Engineers.
Online Training Data Wrangling in Pandas for Machine Learning Engineers
Review from similar course:”The course is really impressive. Tons of information, and I learned a great deal. I had no Python background, and now I feel a lot more confident about working with Python than ever. Thanks for the course.” Austin “Honestly Mike your classes speak for themselves. They’re informative, concise and just really well put together. They’re exactly the kind of courses I look for.” -Alex ElCourse DescriptionWelcome toData Wrangling in Pandas for Machine Learning EngineersThis is thesecond course in a seriesdesigned to prepare you forbecoming a machine learning engineer.I’ll keep this updated and listonlythe coursesthat are live.Here is a list of the courses that can betaken right now.Please take them in order.Theknowledgebuilds fromcourse to course.The Complete PythonCourse for Machine Learning EngineersData Wrangling in Pandas for Machine Learning Engineers(This one)Data Visualization in Python for Machine Learning EngineersLearn the single most important skill for the machine learning engineer: Data WranglingA complete understanding of data wrangling vernacular.Pandas from A-Z.The ability to completely cleanse a tabular data set in Pandas.Lab integrated. Please don’t justwatch. Learning is an interactive event. Go over every lab in detail.Real world Interviews Questions.The knowledge builds from course to course in a serial nature. Without the first course many students might struggle with this one. Thank you.Many new to machine learning believe machine learning engineers spend their days building deepneural models in Keras or SciKit-Learn. I hate to be the bearer of bad news but that isnt the case. A recent study from Kaggle determined that 80% of time data scientists and machine learning engineersspend their time cleaning data. The term used for cleaning data in data science circles is called data wrangling. In this course we are going tolearnPandas using alab integrated approach. Programming is something you have todoinorder to master it. Youcan’t read about Pythonand expect to learnit.Pandas is the single most important library for data wrangling in Python.Data wranglingis the process ofprogrammatically transforming data into a format that makes it easier to workwith.This might mean modifying all of the values in a given column in a certain way, or merging multiple columns together. The necessity for data wrangling is often a byproduct of poorly collected or presented data.In the real world data is messy. Very rarely do you have nicely cleansed data sets to point your supervised models against.Keep in mind that 99% of all applied machine learning (real world machine learning)is supervised. That simply means models need really clean, nicely formatted data. Bad data in means bad model resultsout. **Five Reasons to Take this Course**1) You Want to be a Machine Learning EngineerIt’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 data wrangling in Python you’ll have a hard time of securing a position as a machine learning engineer.2)Most of Machine Learning is Data WranglingIf you’re new to this space the one thingmany won’t tell you is that much of the job of the data scientist and the machine learning engineer is massaging dirty data into a state where it can be modeled. In the real world data is dirty and before you can build accurate machine learning models you have to clean it. This process is called data wrangling and without this skills set you’ll never get a job as a machine learning engineer. This course will give you the fundamentals you need to cleanse your data.3)The Growth of Data is InsaneNinety 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. Python has libraries that are specific to data cleansing.4) Machine Learning in Plain EnglishMachine 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.5) You want to be ahead of the CurveThe data engineer and machine learning engineer rolesarefairly 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 interest inData Wrangling in Pandas for Machine Learning EngineersSee you 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.