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 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. 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
- 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 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 onlythe courses that are live. Here is a list of the courses that can be taken right now. Please take them in order.The knowledgebuilds 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 buildsfrom course to course in a serial nature. Withoutthe 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 learnin 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
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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
Frequently Asked Questions
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