Support Vector Machines in Python
Learn about creating support vector machines in Python for use in machine learning in this free online course.
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Support vector machines (SVMs) are being increasingly used in machine learning for their near-accurate predictions and data interpretations. This course helps you understand and apply this advanced machine learning technique in your day-to-day data analysis activities using the Python programming language. The video-based lessons will make the complex machine learning algorithms easy for you to understand. For those who are new to Python, a crash course is included to show you how to install Python and Anaconda software and introduces you to Python commands and libraries. All the steps involved in solving a business problem through a decision tree are covered in this course. Although the focus is on teaching you how to run the analysis, this course also teaches you how to select the correct data, how to preprocess the data, and after running the analysis, judge the quality of your model.
You will be using the Jupyter Notebook application provided by Anaconda to follow the videos’ steps. Once you have downloaded and set up Anaconda and Python, you will learn machine learning basics. The course explains what machine learning is, how it is related to SVM, and what its applicability is in different business scenarios. You will be introduced to the concept of hyperplanes and the maximum margin classifier. You will see that this classifier has limitations, which will lead you through the concepts of the support vector classifier to the kernel-based support vector machines. Next, you will follow the steps of importing data into the Jupyter environment, tidying up and preprocessing the data, splitting it up for training and testing purposes and finally building the SVM models. You will begin by building a regression model and then move on to building classification models. You will learn how to tune the hyperparameters for potentially better and more accurate results. You will also learn to build advanced SVM models with polynomial and radial kernels and their hyperparameter tuning.
Does the idea of learning advanced SVM techniques starting from scratch intrigue you? Would you like the confidence to get ahead in your field of expertise? Then this course is for you! It guides you through everything you need to know to create an SVM model in Python. There are no prerequisites to enrol for this course, although an understanding of statistical methods may be helpful. After completing this course, you will be able to apply machine learning and SVM techniques to real-life business problems. This course will help you grow professionally and enhance your career prospects in data science. So, why wait any longer? Dive into the course and be proficient in the classification skill everyone is raving about.Start Course Now
Python Crash Course
Python Crash Course - Learning Outcomes
Anaconda and Jupyter
Python Crash Course - Lesson Summary
Basics of Machine Learning and SVM
Basics of Machine Learning and SVM - Learning Outcomes
Machine Learning Basics
Maximum Margin Classifier
Support Vector Classifier
Basics of Machine Learning and SVM - Lesson Summary
Building SVM Models in Python
Building SVM Models in Python - Learning Outcomes
SVM-Based Regression Model
Building SVM Models in Python - Lesson Summary
After completing this course you should be able to:
- Outline the steps to Python and Jupyter on your computer
- Identify the sequence of steps to create a machine learning model
- Explain the concept of a hyperplane
- Distinguish between a maximum margin classifier and a support vector classifier
- Compare the different types of kernels
- Recall the steps to train a classification SVM model using linear, polynomial and radial kernels
- Discuss the need for hyperparameter tuning in SVM
- Describe the process of training a regression SVM model
- Distinguish between the datasets used in classification and regression SVM models
All Alison courses are free to enrol, study and complete. To successfully complete this Certificate course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Certificate course, you have the option to acquire an official Certificate, which is a great way to share your achievement with the world. Your Alison Certificate is:
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