Support Vector Machines in Python SVM Concepts Code

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning
Support Vector Machines in Python SVM Concepts Code
File Size :
2.18 GB
Total length :
6h 15m



Start-Tech Academy


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Support Vector Machines in Python SVM Concepts Code

What you’ll 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

Support Vector Machines in Python SVM Concepts Code


Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same


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 – JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. – DaisyOur PromiseTeaching 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 AssignmentsWith 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!CheersStart-Tech Academy


Section 1: Introduction

Lecture 1 Introduction

Section 2: Setting up Python and Python Crash Course

Lecture 2 Installing Python and Anaconda

Lecture 3 Course Resources

Lecture 4 Opening Jupyter Notebook

Lecture 5 This is a milestone!

Lecture 6 Introduction to Jupyter

Lecture 7 Arithmetic operators in Python: Python Basics

Lecture 8 String in Python – Part 1

Lecture 9 Strings in Python – Part 2

Lecture 10 Lists, Tuples and Directories: Python Basics

Lecture 11 Working with Numpy Library of Python

Lecture 12 Working with Pandas Library of Python

Lecture 13 Working with Seaborn Library of Python

Section 3: Machine Learning Basics

Lecture 14 Introduction to Machine Learning

Lecture 15 Building a Machine Learning Model

Section 4: Maximum Margin Classifier

Lecture 16 Course flow

Lecture 17 The Concept of a Hyperplane

Lecture 18 Maximum Margin Classifier

Lecture 19 Limitations of Maximum Margin Classifier

Section 5: Support Vector Classifier

Lecture 20 Support Vector classifiers

Lecture 21 Limitations of Support Vector Classifiers

Section 6: Support Vector Machines

Lecture 22 Kernel Based Support Vector Machines

Section 7: Creating Support Vector Machine Model in Python

Lecture 23 Regression and Classification Models

Lecture 24 The Data set for the Regression problem

Lecture 25 Importing data for regression model

Lecture 26 Missing value treatment

Lecture 27 Dummy Variable creation

Lecture 28 X-y Split

Lecture 29 Test-Train Split

Lecture 30 More about test-train split

Lecture 31 Standardizing the data

Lecture 32 SVM based Regression Model in Python

Lecture 33 The Data set for the Classification problem

Lecture 34 Classification model – Preprocessing

Lecture 35 Classification model – Standardizing the data

Lecture 36 SVM Based classification model

Lecture 37 Hyper Parameter Tuning

Lecture 38 Polynomial Kernel with Hyperparameter Tuning

Lecture 39 Radial Kernel with Hyperparameter Tuning

Section 8: Appendix 1: Data Preprocessing

Lecture 40 Gathering Business Knowledge

Lecture 41 Data Exploration

Lecture 42 The Dataset and the Data Dictionary

Lecture 43 Importing Data in Python

Lecture 44 Univariate analysis and EDD

Lecture 45 EDD in Python

Lecture 46 Outlier Treatment

Lecture 47 Outlier Treatment in Python

Lecture 48 Missing Value Imputation

Lecture 49 Missing Value Imputation in Python

Lecture 50 Seasonality in Data

Lecture 51 Bi-variate analysis and Variable transformation

Lecture 52 Variable transformation and deletion in Python

Lecture 53 Non-usable variables

Lecture 54 Dummy variable creation: Handling qualitative data

Lecture 55 Dummy variable creation in Python

Lecture 56 Correlation Analysis

Lecture 57 Correlation Analysis in Python

Section 9: Bonus Section

Lecture 58 The final milestone!

Lecture 59 Bonus Lecture

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

Course Information:

Udemy | English | 6h 15m | 2.18 GB
Created by: Start-Tech Academy

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