Support Vector Machines in Python- SVM Concepts Code_1

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 models 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 models 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

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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