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Nome:Udemy - Data Wrangling in Pandas for Machine Learning Engineers
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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 El

Course Description

Welcome to Data Wrangling in Pandas for Machine Learning Engineers

This is the second course in a series designed to prepare you for becoming 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
   Data Wrangling in Pandas for Machine Learning Engineers (This one)
   Data Visualization in Python for Machine Learning Engineers

Learn the single most important skill for the machine learning engineer: Data Wrangling

   A 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 just watch. 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 deep neural models in Keras or SciKit-Learn. I hate to be the bearer of bad news but that isn’t the case.

A recent study from Kaggle determined that 80% of time data scientists and machine learning engineers spend their time cleaning data. The term used for cleaning data in data science circles is called data wrangling.

In this course we are going to learn Pandas 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 learn it.

Pandas is the single most important library for data wrangling in Python.

Data wrangling is the process of programmatically transforming data into a format that makes it easier to work with.

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 results out.

                                                          **Five Reasons to Take this Course**

1) You Want to be a Machine Learning Engineer

It’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 Wrangling

If you’re new to this space the one thing many 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 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.  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 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 and their machine learning engineers to be able to build machine learning models.

5) You want to be ahead of the Curve

The data engineer and machine learning engineer roles are fairly new.  While you’re learning, building your skills and becoming certified you are also the first to be part of this burgeoning field.  You know that the first to be certified means the first to be hired and first to receive the top compensation package.

Thanks for interest in Data Wrangling in Pandas for Machine Learning Engineers

See you in the course!!
Who this course is for:

   If you’re interested in becoming a machine learning engineer then this course is for you.
   If you’re interested in becoming a data engineer then this course is for you.
   If you’re a professional in any discipline that needs to become adept at data wrangling then this course is for you.

Requirements

   If you haven’t already please take The Complete Python Course for Machine Learning Engineers
   This is the second course in a series of courses. Each course builds on one another from a knowledge perspective.
   A basic understanding of machine learning would be beneficial.

Last updated 2/2018
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Categoria:Tutorials
Idioma:English  English
Total Size:125.92 MB
Informações Hash:C565857F5368C06166BE0919184D2A22C3E08C4A
Adicionado por:tutsgalaxy Verified Uploader
Data adicionada:2019-07-13 15:53:54
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