Support Vector Machines in Python: SVM Concepts & Code
Support Vector Machines in Python: SVM Concepts & Code, available at $74.99, has an average rating of 4.45, with 67 lectures, 11 quizzes, based on 518 reviews, and has 89472 subscribers.
You will learn about Get a solid understanding of Support Vector Machines (SVM) Understand the business scenarios where Support Vector Machines (SVM) is applicable Tune a machine learning model's hyperparameters and evaluate its performance. Use Support Vector Machines (SVM) to make predictions Implementation of SVM models in Python This course is ideal for individuals who are People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master SVM technique from Beginner to Advanced in short span of time It is particularly useful for People pursuing a career in data science or Working Professionals beginning their Data journey or Statisticians needing more practical experience or Anyone curious to master SVM technique from Beginner to Advanced in short span of time.
Enroll now: Support Vector Machines in Python: SVM Concepts & Code
Summary
Title: Support Vector Machines in Python: SVM Concepts & Code
Price: $74.99
Average Rating: 4.45
Number of Lectures: 67
Number of Quizzes: 11
Number of Published Lectures: 62
Number of Published Quizzes: 10
Number of Curriculum Items: 78
Number of Published Curriculum Objects: 72
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Get a solid understanding of Support Vector Machines (SVM)
- Understand the business scenarios where Support Vector Machines (SVM) is applicable
- Tune a machine learning model's hyperparameters and evaluate its performance.
- Use Support Vector Machines (SVM) to make predictions
- Implementation of SVM models in Python
Who Should Attend
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience
- Anyone curious to master SVM technique from Beginner to Advanced in short span of time
Target Audiences
- People pursuing a career in data science
- Working Professionals beginning their Data journey
- Statisticians needing more practical experience
- Anyone curious to master SVM technique from Beginner to Advanced in short span of time
You’re looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right?
You’ve found the right Support Vector Machines techniques course!
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through Decision tree.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
Go ahead and click the enroll button, and I’ll see you in lesson 1!
Cheers
Start-Tech Academy
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Chapter 2: Setting up Python and Python Crash Course
Lecture 1: Installing Python and Anaconda
Lecture 2: Course Resources
Lecture 3: Opening Jupyter Notebook
Lecture 4: This is a milestone!
Lecture 5: Introduction to Jupyter
Lecture 6: Arithmetic operators in Python: Python Basics
Lecture 7: String in Python – Part 1
Lecture 8: Strings in Python – Part 2
Lecture 9: Lists, Tuples and Directories: Python Basics
Lecture 10: Working with Numpy Library of Python
Lecture 11: Working with Pandas Library of Python
Lecture 12: Working with Seaborn Library of Python
Lecture 13: Python file for additional practice
Chapter 3: Integrating ChatGPT with Python
Lecture 1: Integrating ChatGPT with Jupyter Notebook
Chapter 4: Machine Learning Basics
Lecture 1: Introduction to Machine Learning
Lecture 2: Building a Machine Learning Model
Chapter 5: Maximum Margin Classifier
Lecture 1: Course flow
Lecture 2: The Concept of a Hyperplane
Lecture 3: Maximum Margin Classifier
Lecture 4: Limitations of Maximum Margin Classifier
Chapter 6: Support Vector Classifier
Lecture 1: Support Vector classifiers
Lecture 2: Limitations of Support Vector Classifiers
Chapter 7: Support Vector Machines
Lecture 1: Kernel Based Support Vector Machines
Chapter 8: Creating Support Vector Machine Model in Python
Lecture 1: Regression and Classification Models
Lecture 2: The Data set for the Regression problem
Lecture 3: Importing data for regression model
Lecture 4: Missing value treatment
Lecture 5: Dummy Variable creation
Lecture 6: X-y Split
Lecture 7: Test-Train Split
Lecture 8: More about test-train split
Lecture 9: Standardizing the data
Lecture 10: SVM based Regression Model in Python
Lecture 11: The Data set for the Classification problem
Lecture 12: Classification model – Preprocessing
Lecture 13: Classification model – Standardizing the data
Lecture 14: SVM Based classification model
Lecture 15: Hyper Parameter Tuning
Lecture 16: Polynomial Kernel with Hyperparameter Tuning
Lecture 17: Radial Kernel with Hyperparameter Tuning
Chapter 9: Appendix 1: Data Preprocessing
Lecture 1: Gathering Business Knowledge
Lecture 2: Data Exploration
Lecture 3: The Dataset and the Data Dictionary
Lecture 4: Importing Data in Python
Lecture 5: Univariate analysis and EDD
Lecture 6: EDD in Python
Lecture 7: Outlier Treatment
Lecture 8: Outlier Treatment in Python
Lecture 9: Missing Value Imputation
Lecture 10: Missing Value Imputation in Python
Lecture 11: Seasonality in Data
Lecture 12: Bi-variate analysis and Variable transformation
Lecture 13: Variable transformation and deletion in Python
Lecture 14: Non-usable variables
Lecture 15: Dummy variable creation: Handling qualitative data
Lecture 16: Dummy variable creation in Python
Lecture 17: Correlation Analysis
Lecture 18: Correlation Analysis in Python
Chapter 10: Congratulations & about your certificate
Lecture 1: The final milestone!
Lecture 2: About your certificate
Lecture 3: Bonus Lecture
Instructors
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Start-Tech Academy
5,000,000+ Enrollments | 4.5 Rated | 160+ Countries
Rating Distribution
- 1 stars: 11 votes
- 2 stars: 18 votes
- 3 stars: 80 votes
- 4 stars: 188 votes
- 5 stars: 221 votes
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