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