Data Visualization in Python for Machine Learning Engineers
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 order. The 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.
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!!
In this lesson let's learn what this course is about.
Is this course for you? In this lesson let's find out if you're my target audience.
The craft the most basic plot we can in matplotlib.
Why use matplotlib? What function does the library really provide us?
In this lesson let's find out.
Numpy is a numerical library in Python?
Why do we need if for matplotlib?
Let's find out in this lesson.
Let's get our hands dirty and create our very first plot.
Plotting in Matplotlib
In this lesson let's learn how to plot multiple curves.
Instead of creating a dataset to use let's pull some data from a file and use it for plotting.
Let's create a simple scatterplot in this lesson.
int this lab let's get our hands dirty with a scatterplot.
In this lesson let's learn how to craft a bar chart.
Customizing Our Charts
Let's learn about the core styles on our charts.
In this lesson let's add some color to our scatterplot.
Let's learn about plotting from a file in this lesson.
In this lesson let's learn about the edgecolor parameter.
Let's create a bar chart with some color.
In this lab let's chart a bar or two.
Let's learn about the pie chart in this lesson.
Let's compare two boxplots.
Let's learn how to control our line thickness.
Let's get our hands dirty filling our bars with patterns.
What's a marker?
Let's find out in this short lesson.
Let's work through a lab on markers in this lesson.
We can easily control how often our markers show up on our charts.
We can also create custom markers.
Let's learn how in this lesson.
Let's play with the size parameter in this lesson.
Let's learn how to alter our color schemes.
Let's learn how to save a graph to disk.
Let's save a graph to PDF.
In this lecture let's learn how to put simple text on our graphs.
Let's label our axes in this lesson.
In this lab let's add some text to a graph.
In this lesson let's add bounded box to our graph.
In this brief lesson let's learn how to add an arrow to our charts.
Let's work through this lab on grids.
Let's add some ticks to our charts.
Let's learn how to label our ticks in this lesson.
Let's add ticks... the easy way... in this super short lesson.
What is Seaborn?
Let's start working with Seaborn.
Let's create a factorplot in Seaborn.
Let's craft a simple color map.
Let's scale our charts for sundry purposes.
Let's control the size of our fonts in this lab.
Let's learn about the two core functions in Python.
Let's begin working with our axes.
Let's work through a quick lab on controlling the size of our bars.
Let's rotate the text on our axes.