Data Visualization in Python for Machine Learning Engineers

Focused View

Mike West

43:37

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1. Introduction

1. Introduction.mp4

01:18

2. Is this Course for You.mp4

01:07

3. Hello World in matplotlib.mp4

01:46

4. Matplotlib Philosopy.mp4

01:18

5. Numpy.mp4

01:21

6. Lab First Plot.html

7. Summary.html

8. Quiz.html

2. Plotting in Matplotlib

1. Plotting Multiple Curves.mp4

01:22

2.1 matplot.txt

2. Plotting Curves from an Existing Data Set.mp4

01:32

3. Plotting Points.mp4

00:43

4. Lab Scatterplot from Pandas Dataframe.html

5. Bar Charts.mp4

01:26

6. Multiple Bar Charts.mp4

01:16

7. Plotting Stacked Bars.mp4

01:00

8. Lab Plotting Multiple Stacked Bars.html

9. The Pie Chart.mp4

00:54

10. Plotting a Histogram.mp4

01:03

11. Lab Plotting a Histogram.html

12. Plotting Boxplots.mp4

01:30

13. Lab Plotting Multiple Box Plots.html

14. Plotting Triangulations.mp4

00:52

15. Summary.html

3. Customizing Our Charts

1. Adding Styles and Colors.mp4

01:49

2. Adding Color to the Scatterplot.mp4

01:24

3.1 Iris.txt

3. Lab Scatter Plot Grey Scale From a File.html

4. EdgeColor Parameter.mp4

00:34

5. Adding Color to a Bar Chart.mp4

00:49

6. Lab Bar Chart on Dependent Values.html

7. Pie Chart Anatomy.mp4

02:27

8. Black and White Boxplots.mp4

00:58

9. Controlling Line Pattern and Thickness.mp4

00:40

10. Lab Controlling Pattern and Fill.html

11. Working with Markers.mp4

01:08

12. Lab Controlling Marker Size.html

13. Lab Controlling Marker Frequency.html

14. Creating Customer Markers.mp4

00:58

15. Lab List as Input for Size Parameter.html

16. Creating Personalized Color Schemes.mp4

01:40

17. Save Graph to PNG or JPEG.mp4

00:51

18. Lab Save Graph to PDF.html

19. Summary.html

20. Quiz.html

4. Annotations

1. Simple Title Annotation.mp4

00:42

2. Labeling the X and Y Axes.mp4

00:41

3. Lab Adding Text Anywhere.html

4. Bounded Box Control.mp4

00:49

5. Adding an Arrow to a Chart.mp4

00:46

6. Lab Adding a Grid to a Chart.html

7. Adding Ticks to a Chart.mp4

01:04

8. Lab Labeling our Ticks.html

9. Adding Ticks to Charts (The Easy Way).mp4

00:29

10. Summary.html

11. Quiz.html

5. Seaborn

1. Seaborn Introduction.mp4

01:26

2. Lab Exploring the Sundry Color Schemes.html

3. Creating a Factorplot.mp4

01:39

4. Creating a Simple Colormap.mp4

01:12

5. Scaling our Seaborn Plots.mp4

00:44

6. Lab Controlling Font Size.html

7. The Two Core Functions.mp4

01:25

8. How to Set Figure Size.mp4

00:54

9. Lab Figure Level Functions.html

10. Lab Rotate Text on a Seaborn Plot.html

11. Summary.html

12. Quiz.html

13. Bonus Lecture Tons of Free Machine Learning Content.html

Description

The Third Course in a Series for Mastering Python for Machine Learning Engineers

What You'll Learn?

You'll learn Matplotlib and Seaborn and have a solid understanding of how they are used in applied machine learning.
You'll work through hands on labs that will test the skills you learned in the lessons.
You'll learn all the Python vernacular specific to data visualization you need to take you skills to the next level.
You'll be on your way to becoming a real world machine learning engineer or data engineer.

Who is this for?

If you want to become a machine learning engineer then this course is for you.

If you need to learn Python for machine learning then this course is for you.

If you want to learn how to use matplotlib for real world applications then this course is for you.

What You Need to Know?

You've completed the first two courses in the series.

A desire to learn Python.

A basic understanding of machine learning would be beneficial.

More details

Description
Welcome toÂ Data Visualization in Python for Machine learning engineers.

This is theÂ third course in a seriesÂ designed to prepare you forÂ becoming a machine learning engineer.Â

I'll keep this updated and listÂ onlyÂ the coursesÂ that are live.Â Â Here is a list of the courses that can beÂ taken right now.Â Â Please take them in order.Â TheÂ knowledgeÂ builds fromÂ course to course.Â

The Complete PythonÂ Course for Machine Learning EngineersÂ
Data Wrangling in Pandas for Machine Learning EngineersÂ
Data Visualization in Python for Machine Learning EngineersÂ (This one)Â

The second course in the series is about Data Wrangling. Please take the courses in order.

TheÂ knowledge buildsÂ from course to course in aÂ serial nature.Â WithoutÂ the first course many students might struggle with this one.Â

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 onÂ visualizing.Â Here areÂ a few thingsÂ you'llÂ learnÂ in theÂ course.Â

A complete understanding of data visualization vernacular.
Matplotlib from A-Z.Â
The ability to craft usable charts and graphs for all your machine learning needs.Â
Lab integrated. Please don't justÂ watch. Learning is an interactive event.Â Go over every lab in detail.Â
Real world Interviews Questions.

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â 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.Â

2)Â Data Visualization is a Core Component of Machine Learning

Data 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.Â 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 â€“ and you can experiment with different scenarios by making slight adjustments.Â

3)Â The Growth of Data is InsaneÂ

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.Â Almost all real world machine learning is supervised. That means you point your machine learning models at clean tabular data.Â

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. Â Google expects data engineers and their machine learning engineersÂ to be able to build machine learning models.

5) You want to be ahead of the CurveÂ

The data engineer and machine learning engineer rolesÂ areÂ fairly new. Â While youâ€™re learning, building your skills andÂ becoming certified you areÂ also the first to be part of this burgeoning field. Â You know that the first to be certified meansÂ the first to be hired and first to receive the top compensation package.Â

Thanks for interest inÂ Data Visualization in Python for Machine learning engineers.

See you in the course!!
Who this course is for:
If you want to become a machine learning engineer then this course is for you.
If you need to learn Python for machine learning then this course is for you.
If you want to learn how to use matplotlib for real world applications then this course is for you.

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I'm the founder of LogikBot. I've worked at Microsoft and Uber. I helped design courses for Microsoft's Data Science Certifications. If you're interested in machine learning, I can help. I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman. Over the last five years I've transitioned to the exciting world of applied machine learning. I'm excited to show you what I've learned and help you move into one of the single most important fields in this space. Experience, education and passion I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car. Certifications Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT). Personal Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.

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