Get yourself trained on The Complete Python with this Online Training The Complete Python Course for Machine Learning Engineers.
Online Training The Complete Python Course for Machine Learning Engineers
Reviews”I took a few of your courses and you are an amazing teacher. Your courses have brought me up to speed on how to create databases and how to interact and handle Data Engineers and Data Scientists. I will be forever grateful.” -Tony”By taking this course my perception has changed and now data science for me is more about data wrangling. Thank you, Mike:)” -ArchitWelcome toThe Complete Course for Machine Learning Engineers.This series of courses is the only real world path to attaining a job as a machine learning engineer. Machine learning engineers don’t build models every day.If you want to work in the real world then focus on learning Python. That’s what this course is… Python!!!This is the first course in a series of courses designed to prepare you for a real-world career as a machine learning engineer.I’ll keep this updated and list only the courses that are live. Here is a list of the courses that can be taken right now. Please take them in order. The knowledge builds from course to course.The Complete Python Course for Machine Learning Engineers (This one)Data Wrangling in Pandas for Machine Learning EngineersData Visualization in Python for Machine Learning EngineersSciKit-Learn in Python for Machine Learning Engineers (NEW)In this course we are going to learn Python using a lab integrated approach. Programming is something you have to do in order to master it. You can’t read about Python and expect to learnit.If you take this course from start to finish you’ll know the core foundations of Python, you’ll understand the very basics of data cleansing and lastly you’ll build a traditional machine learning model and a deep learning model.While the course is centered on learning the basics of Python you’ll get to see how data cleansing is applied to a data set and how a traditional machine learning model and a deep learning model are built.This course is an applied course on machine learning. Here’ are a few itemsyou’ll learn:Python basics from A-ZLab integrated. Please don’t justwatch. Learning is an interactive event. Go over every lab in detail.Real world Interviews QuestionsData Wrangling overview. What is it?Pay attention to the basics, it’s what you’ll be doing most of your time.Build a basic model build in SciKit-Learn. We call these traditional models to distinguish themfrom deep learning models.Build a basic Keras model. Keras is becoming the go to Python library for building deep learning models.If you’re new to programming or machine learning you might ask, why would I want to learn Python?Python has become the gold standard for building machine learning models in the applied space. 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 a machine learning engineer,data engineeror data scientistthen you’ll have to know Python. The good news is that Python is a high level language. That means it was designed with ease of learning in mind. It’s very user friendly and has a lot of applications outside of the ones we are interested in.InThe Complete Course for Machine Learning Engineerswe are going to start with the basics. You’ll learn how to install Python all the way through building a simple deep learning model using the skills you’ve learned.As you learn Python you’ll be completing labs that will build on what you’ve learned in the previous lesson so please don’t skip any. *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 Python you’ll have a hard time of securing a position as a machine learning engineer.2)The Google Certified Data EngineerGoogle 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. You can’t become a data engineer without learning Python.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. In this course, you’ll learn enough Python to be able to build a deep learning model.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 inThe Complete Python Course 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.