Coursera
Coursera Logo

Support Vector Machines in Python, From Start to Finish 

  • Offered byCoursera

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Overview

Learn by doing through completing tasks in a split-screen environment directly in your browser

Duration

2 hours

Total fee

729

Mode of learning

Online

Schedule type

Self paced

Difficulty level

Intermediate

Credential

Certificate

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Highlights

  • Get free download of created material
  • Students can access their cloud desktop across six different sessions
  • Get instant access to the necessary software packages through Rhyme
  • A split-screen video walkthrough of each step, from a subject-matter expert
  • Learn from all learning materials, including the interactive workspace and final quiz
Read more
Details Icon

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Course details

Skills you will learn
Who should do this course?
  • For learners who are based in the North America region
What are the course deliverables?
  • Import data into, and manipulating a pandas dataframe
  • Format the data for a support vector machine, including One-Hot Encoding and missing data.
  • Optimize parameters for the radial basis function and classification
  • Build, evaluate, draw and interpret a support vector machine
More about this course
  • This course runs on Coursera's hands-on project platform called Rhyme
  • On Rhyme, students do projects in a hands-on manner in your browser
  • Get instant access to pre-configured cloud desktops containing all of the software and data you need for the project.
  • Everything is already set up directly in Internet browser so you can just focus on learning
  • For this project, students will get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Curriculum

Import the modules that will do all the work

Import the data

Missing Data Part 1: Identifying Missing Data

Missing Data Part 2: Dealing With Missing Data

Format Data Part 1: Split the Data into Dependent and Independent Variables

Format the Data Part 2: One-Hot Encoding

Format the Data Part 3: Centering and Scaling

Build A Preliminary Support Vector Machine

Optimize Parameters with Cross Validation

Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine

Faculty Icon

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Faculty details

Josh Starmer
Josh Starmer makes StatQuest videos on statistics and machine learning. Before that he did computational bio-stuff (statistics, mathematics, computing etc) at the University of North Carolina at Chapel Hill

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Entry Requirements

Eligibility criteriaUp Arrow Icon
Conditional OfferUp Arrow Icon
  • Not mentioned

Other courses offered by Coursera

– / –
3 months
Beginner
– / –
20 hours
Beginner
– / –
2 months
Beginner
– / –
3 months
Beginner
View Other 6715 CoursesRight Arrow Icon

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 
Students Ratings & Reviews

3/5
Verified Icon1 Rating
R
ROHAN LONE
Support Vector Machines in Python, From Start to Finish
Offered by Coursera
3
Other: Understood the theory behind support vector machines Built SVM models with scikit-learn to classify linear and non-linear data Determined the strengths and limitations of SVMs
Reviewed on 19 Sep 2021Read More
Thumbs Up IconThumbs Down Icon
View 1 ReviewRight Arrow Icon
qna

Support Vector Machines in Python, From Start to Finish
 at 
Coursera 

Student Forum

chatAnything you would want to ask experts?
Write here...