Data Visualization in Python for Machine Learning Engineers
3.9 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
10,205 students enrolled

Data Visualization in Python for Machine Learning Engineers

The Third Course in a Series for Mastering Python for Machine Learning Engineers
3.9 (162 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
10,205 students enrolled
Created by Mike West
Last updated 3/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
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30-Day Money-Back Guarantee
This course includes
  • 44 mins on-demand video
  • 25 articles
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
Course content
Expand all 63 lectures 01:06:58
+ Introduction
7 lectures 08:02

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

Preview 01:18

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

Preview 01:07

The craft the most basic plot we can in matplotlib. 

Preview 01:46

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

In this lesson let's find out. 

Preview 01:18

Numpy is a numerical library in Python? 

Why do we need if for matplotlib? 

Let's find out in this lesson. 

Preview 01:20

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

Preview 00:57
Summary
00:16
Quiz
5 questions
+ Plotting in Matplotlib
15 lectures 16:35

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

Plotting Multiple Curves
01:21

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

Plotting Curves from an Existing Data Set
01:32

Let's create a simple scatterplot in this lesson. 

Plotting Points
00:43

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

Lab: Scatterplot from Pandas Dataframe
01:08

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

Bar Charts
01:26
Multiple Bar Charts
01:16
Plotting Stacked Bars
01:00
Lab: Plotting Multiple Stacked Bars
01:06
The Pie Chart
00:54
Plotting a Histogram
01:03
Lab: Plotting a Histogram
01:04
Plotting Boxplots
01:30
Lab: Plotting Multiple Box Plots
00:41
Plotting Triangulations
00:52
Summary
00:58
+ Customizing Our Charts
19 lectures 22:03

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

Adding Styles and Colors
01:49

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

Adding Color to the Scatterplot
01:24

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

Lab: Scatter Plot Grey Scale From a File
02:10

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

EdgeColor Parameter
00:34

Let's create a bar chart with some color. 

Adding Color to a Bar Chart
00:49

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

Lab: Bar Chart on Dependent Values
01:20

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

Pie Chart Anatomy
02:27

Let's compare two boxplots. 

Black and White Boxplots
00:57

Let's learn how to control our line thickness. 

Controlling Line Pattern and Thickness
00:40

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

Lab: Controlling Pattern and Fill
00:53

What's a marker? 

Let's find out in this short lesson. 

Working with Markers
01:08

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

Lab: Controlling Marker Size
01:02

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

Lab: Controlling Marker Frequency
00:52

We can also create custom markers. 

Let's learn how in this lesson. 

Creating Customer Markers
00:57

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

Lab: List as Input for Size Parameter
00:48

Let's learn how to alter our color schemes. 

Creating Personalized Color Schemes
01:40

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

Save Graph to PNG or JPEG
00:51

Let's save a graph to PDF.

Lab: Save Graph to PDF
00:42
Summary
00:55
Quiz
8 questions
+ Annotations
10 lectures 07:46

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

Simple Title Annotation
00:42

Let's label our axes in this lesson. 

Labeling the X and Y Axes
00:41

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

Lab: Adding Text Anywhere
00:55

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

Bounded Box Control
00:49

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

Adding an Arrow to a Chart
00:46

Let's work through this lab on grids. 

Lab: Adding a Grid to a Chart
00:51

Let's add some ticks to our charts. 

Adding Ticks to a Chart
01:04

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

Lab: Labeling our Ticks
01:05

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

Adding Ticks to Charts (The Easy Way)
00:29
Summary
00:23
Quiz
6 questions
+ Seaborn
12 lectures 12:30

What is Seaborn? 

Seaborn Introduction
01:26

Let's start working with Seaborn. 

Lab: Exploring the Sundry Color Schemes
00:45

Let's create a factorplot in Seaborn. 

Creating a Factorplot
01:38

Let's craft a simple color map. 

Creating a Simple Colormap
01:12

Let's scale our charts for sundry purposes. 

Scaling our Seaborn Plots
00:44

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

Lab: Controlling Font Size
01:12

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

The Two Core Functions
01:25

Let's begin working with our axes. 

How to Set Figure Size
00:54

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

Lab: Figure Level Functions
00:51

Let's rotate the text on our axes. 

Lab: Rotate Text on a Seaborn Plot
01:09
Summary
00:49
Quiz
6 questions
Bonuse Lecture - On to SciKit-Learn and/or XGBoost
00:23
Requirements
  • You've completed the first two courses in the series.
  • A desire to learn Python.
  • A basic understanding of machine learning would be beneficial.
Description

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!!

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.