Data Visualization in Python for Machine Learning Engineers

Data Visualization in Python for Machine Learning Engineers, available at $19.99, has an average rating of 4.45, with 63 lectures, 4 quizzes, based on 181 reviews, and has 10263 subscribers.

You will learn about Youll learn Matplotlib and Seaborn and have a solid understanding of how they are used in applied machine learning. Youll work through hands on labs that will test the skills you learned in the lessons. Youll learn all the Python vernacular specific to data visualization you need to take you skills to the next level. Youll be on your way to becoming a real world machine learning engineer or data engineer. This course is ideal for individuals who are If you want to become a machine learning engineer then this course is for you. or If you need to learn Python for machine learning then this course is for you. or If you want to learn how to use matplotlib for real world applications then this course is for you. It is particularly useful for If you want to become a machine learning engineer then this course is for you. or If you need to learn Python for machine learning then this course is for you. or If you want to learn how to use matplotlib for real world applications then this course is for you.

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Summary

Title: Data Visualization in Python for Machine Learning Engineers

Price: $19.99

Average Rating: 4.45

Number of Lectures: 63

Number of Quizzes: 4

Number of Published Lectures: 63

Number of Published Quizzes: 4

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 67

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Youll learn Matplotlib and Seaborn and have a solid understanding of how they are used in applied machine learning.

Youll work through hands on labs that will test the skills you learned in the lessons.

Youll learn all the Python vernacular specific to data visualization you need to take you skills to the next level.

Youll be on your way to becoming a real world machine learning engineer or data engineer.

Who Should Attend

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.

Target Audiences

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.

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. Pleasetake 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!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Is this Course for You?

Lecture 3: Hello World in matplotlib

Lecture 4: Matplotlib Philosopy

Lecture 5: Numpy

Lecture 6: Lab: First Plot

Lecture 7: Summary

Chapter 2: Plotting in Matplotlib

Lecture 1: Plotting Multiple Curves

Lecture 2: Plotting Curves from an Existing Data Set

Lecture 3: Plotting Points

Lecture 4: Lab: Scatterplot from Pandas Dataframe

Lecture 5: Bar Charts

Lecture 6: Multiple Bar Charts

Lecture 7: Plotting Stacked Bars

Lecture 8: Lab: Plotting Multiple Stacked Bars

Lecture 9: The Pie Chart

Lecture 10: Plotting a Histogram

Lecture 11: Lab: Plotting a Histogram

Lecture 12: Plotting Boxplots

Lecture 13: Lab: Plotting Multiple Box Plots

Lecture 14: Plotting Triangulations

Lecture 15: Summary

Chapter 3: Customizing Our Charts

Lecture 1: Adding Styles and Colors

Lecture 2: Adding Color to the Scatterplot

Lecture 3: Lab: Scatter Plot Grey Scale From a File

Lecture 4: EdgeColor Parameter

Lecture 5: Adding Color to a Bar Chart

Lecture 6: Lab: Bar Chart on Dependent Values

Lecture 7: Pie Chart Anatomy

Lecture 8: Black and White Boxplots

Lecture 9: Controlling Line Pattern and Thickness

Lecture 10: Lab: Controlling Pattern and Fill

Lecture 11: Working with Markers

Lecture 12: Lab: Controlling Marker Size

Lecture 13: Lab: Controlling Marker Frequency

Lecture 14: Creating Customer Markers

Lecture 15: Lab: List as Input for Size Parameter

Lecture 16: Creating Personalized Color Schemes

Lecture 17: Save Graph to PNG or JPEG

Lecture 18: Lab: Save Graph to PDF

Lecture 19: Summary

Chapter 4: Annotations

Lecture 1: Simple Title Annotation

Lecture 2: Labeling the X and Y Axes

Lecture 3: Lab: Adding Text Anywhere

Lecture 4: Bounded Box Control

Lecture 5: Adding an Arrow to a Chart

Lecture 6: Lab: Adding a Grid to a Chart

Lecture 7: Adding Ticks to a Chart

Lecture 8: Lab: Labeling our Ticks

Lecture 9: Adding Ticks to Charts (The Easy Way)

Lecture 10: Summary

Chapter 5: Seaborn

Lecture 1: Seaborn Introduction

Lecture 2: Lab: Exploring the Sundry Color Schemes

Lecture 3: Creating a Factorplot

Lecture 4: Creating a Simple Colormap

Lecture 5: Scaling our Seaborn Plots

Lecture 6: Lab: Controlling Font Size

Lecture 7: The Two Core Functions

Lecture 8: How to Set Figure Size

Lecture 9: Lab: Figure Level Functions

Lecture 10: Lab: Rotate Text on a Seaborn Plot

Lecture 11: Summary

Lecture 12: Bonus Lecture: Tons of Free Machine Learning Content

Instructors

Mike West
Creator of LogikBot

Rating Distribution

1 stars: 3 votes

2 stars: 5 votes

3 stars: 36 votes

4 stars: 68 votes

5 stars: 69 votes

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