Get yourself trained on Data Visualization in with this Online Training Data Visualization in Python for Machine Learning Engineers.
Online Training Data Visualization in Python for Machine Learning Engineers
Welcome toData Visualization in Python for Machine learning engineers.This is thethird 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 EngineersData Visualization in Python for Machine Learning Engineers(This one)The second course in the series is about Data Wrangling. Please take the courses in order. Theknowledge buildsfrom course to course in aserial nature.Withoutthe first course many students might struggle with this one. Thank you!! In this course we are going to focus on data visualization and in Python that means we are going to be learning matplotlib and seaborn. Matplotlib is a Python package for 2D plotting that generates production-quality graphs. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc., with just a few lines of code. Seaborn is a Python visualization library based on matplotlib. Most developers will use seaborn if the same functionally exists in both matplotlib and seaborn. This course focuses onvisualizing.Here area few thingsyou’lllearnin thecourse. A complete understanding of data visualization vernacular. Matplotlib from A-Z. The ability to craft usable charts and graphs for all your machine learning needs. Lab integrated. Please don’t justwatch. Learning is an interactive event. Go over every lab in detail. Real world Interviews Questions. **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)Data Visualization is a Core Component of Machine LearningData visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns.Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner and you can experiment with different scenarios by making slight adjustments.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.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 Visualization in Python for Machine learning engineers.See 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.