4.45 out of 5
4.45
11 reviews on Udemy

Data Visualization in Python for Machine Learning Engineers

The Third Course in a Series for Mastering Python for Machine Learning Engineers
Instructor:
Mike West
161 students enrolled
English [Auto-generated]
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.

Welcome to Data Visualization in Python for Machine learning engineers.

This is the third 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 orderThe knowledge builds from course to course. 

  • The Complete Python Course for Machine Learning Engineers 
  • Data Wrangling in Pandas for Machine Learning Engineers 
  • Data 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.

The knowledge builds from course to course in a serial nature. Without the 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 on visualizing. Here are a few things you’ll learn in the course

  • 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 just watch. 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 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) 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. 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 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. 

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 Visualization in Python for Machine learning engineers.

See you in the course!!

Introduction

1
Introduction

In this lesson let's learn what this course is about. 

2
Is this Course for You?

Is this course for you?  In this lesson let's find out if you're my target audience. 

3
Hello World in matplotlib

The craft the most basic plot we can in matplotlib. 

4
Matplotlib Philosopy

Why use matplotlib? What function does the library really provide us? 

In this lesson let's find out. 

5
Numpy

Numpy is a numerical library in Python? 

Why do we need if for matplotlib? 

Let's find out in this lesson. 

6
Lab: First Plot

Let's get our hands dirty and create our very first plot. 

7
Summary
8
Quiz

Plotting in Matplotlib

1
Plotting Multiple Curves

In this lesson let's learn how to plot multiple curves. 

2
Plotting Curves from an Existing Data Set

Instead of creating a dataset to use let's pull some data from a file and use it for plotting. 

3
Plotting Points

Let's create a simple scatterplot in this lesson. 

4
Lab: Scatterplot from Pandas Dataframe

int this lab let's get our hands dirty with a scatterplot. 

5
Bar Charts

In this lesson let's learn how to craft a bar chart. 

6
Multiple Bar Charts
7
Plotting Stacked Bars
8
Lab: Plotting Multiple Stacked Bars
9
The Pie Chart
10
Plotting a Histogram
11
Lab: Plotting a Histogram
12
Plotting Boxplots
13
Lab: Plotting Multiple Box Plots
14
Plotting Triangulations
15
Summary

Customizing Our Charts

1
Adding Styles and Colors

Let's learn about the core styles on our charts. 

2
Adding Color to the Scatterplot

In this lesson let's add some color to our scatterplot. 

3
Lab: Scatter Plot Grey Scale From a File

Let's learn about plotting from a file in this lesson. 

4
EdgeColor Parameter

In this lesson let's learn about the edgecolor parameter. 

5
Adding Color to a Bar Chart

Let's create a bar chart with some color. 

6
Lab: Bar Chart on Dependent Values

In this lab let's chart a bar or two. 

7
Pie Chart Anatomy

Let's learn about the pie chart in this lesson. 

8
Black and White Boxplots

Let's compare two boxplots. 

9
Controlling Line Pattern and Thickness

Let's learn how to control our line thickness. 

10
Lab: Controlling Pattern and Fill

Let's get our hands dirty filling our bars with patterns.

11
Working with Markers

What's a marker? 

Let's find out in this short lesson. 

12
Lab: Controlling Marker Size

Let's work through a lab on markers in this lesson. 

13
Lab: Controlling Marker Frequency

We can easily control how often our markers show up on our charts. 

14
Creating Customer Markers

We can also create custom markers. 

Let's learn how in this lesson. 

15
Lab: List as Input for Size Parameter

Let's play with the size parameter in this lesson. 

16
Creating Personalized Color Schemes

Let's learn how to alter our color schemes. 

17
Save Graph to PNG or JPEG

Let's learn how to save a graph to disk. 

18
Lab: Save Graph to PDF

Let's save a graph to PDF.

19
Summary
20
Quiz

Annotations

1
Simple Title Annotation

In this lecture let's learn how to put simple text on our graphs. 

2
Labeling the X and Y Axes

Let's label our axes in this lesson. 

3
Lab: Adding Text Anywhere

In this lab let's add some text to a graph. 

4
Bounded Box Control

In this lesson let's add bounded box to our graph. 

5
Adding an Arrow to a Chart

In this brief lesson let's learn how to add an arrow to our charts. 

6
Lab: Adding a Grid to a Chart

Let's work through this lab on grids. 

7
Adding Ticks to a Chart

Let's add some ticks to our charts. 

8
Lab: Labeling our Ticks

Let's learn how to label our ticks in this lesson. 

9
Adding Ticks to Charts (The Easy Way)

Let's add ticks... the easy way... in this super short lesson. 

10
Summary
11
Quiz

Seaborn

1
Seaborn Introduction

What is Seaborn? 

2
Lab: Exploring the Sundry Color Schemes

Let's start working with Seaborn. 

3
Creating a Factorplot

Let's create a factorplot in Seaborn. 

4
Creating a Simple Colormap

Let's craft a simple color map. 

5
Scaling our Seaborn Plots

Let's scale our charts for sundry purposes. 

6
Lab: Controlling Font Size

Let's control the size of our fonts in this lab. 

7
The Two Core Functions

Let's learn about the two core functions in Python. 

8
How to Set Figure Size

Let's begin working with our axes. 

9
Lab: Figure Level Functions

Let's work through a quick lab on controlling the size of our bars. 

10
Lab: Rotate Text on a Seaborn Plot

Let's rotate the text on our axes. 

11
Summary
12
Quiz
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.5
4.5 out of 5
11 Ratings

Detailed Rating

Stars 5
5
Stars 4
4
Stars 3
2
Stars 2
0
Stars 1
0
29f66d0dc3f1308da92166e4b809b9e4
30-Day Money-Back Guarantee

Includes

1 hours on-demand video
24 articles
Full lifetime access
Access on mobile and TV
Certificate of Completion