Support Vector Machine A-Z: Support Vector Machine Python ©

Support Vector Machine A-Z: Support Vector Machine Python ©

Are you ready to start your path to becoming a Machine Learning expert!

Are you ready to train your machine like a father trains his son!

A breakthrough in Machine Learning would be worth ten Microsofts." -Bill Gates

There are lots of courses and lectures out there regarding Support Vector Machine. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already been learned and move one extra step forward and then we assign you a small task that is solved at the beginning of the next video.

We start by teaching the theoretical part of the concept and then we implement everything as it is practically using python

This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like humans and based on that learning, your machine starts making predictions as well!

We’ll be using python as a programming language in this course which is the hottest language nowadays if we talk about machine learning. Python will be taught from a very basic level up to an advanced level so that any machine learning concept can be implemented.

We’ll also learn various steps of data preprocessing which allows us to make data ready for machine learning algorithms.

We’ll learn all general concepts of machine learning overall which will be followed by the implementation of one of the most important ML algorithms “Support Vector Machine”. Each and every concept of SVM will be taught theoretically and will be implemented using python.

Machine learning has been ranked one of the hottest jobs on Glassdoor and the average salary of a machine learning engineer is over $110,000 in the United States according to Indeed! Machine Learning is a rewarding career that allows you to solve some of the world's most interesting problems!

This course is designed for both beginners with some programming experience or even those who know nothing about ML and SVM!

This comprehensive course is comparable to other Machine Learning courses that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 11 hours of HD video lectures divided into 70+ small videos and detailed code notebooks for every lecture, this is one of the most comprehensive courses for Logistic regression and machine learning on Udemy!

This course is really special for you because we are teaching everything from the beginning and for those who want to go an extra mile and want to learn maths behind SVM, there is a special gift for those people as well.

We'll teach you how to program with Python, how to use it for data preprocessing and SVM! Here are just a few of the topics that we will be learning:

Programming with Python

NumPy with Python for array handling

Using pandas Data Frames to handle Excel Files

Use matplotlib for data visualizations

Data Preprocessing

Machine Learning concepts, including:

• Model fitting

• Overfitting

• Model Validation

• Data snooping

• Data encoding

SVM with sk-learn

SVM from absolute scratch using NumPy

Implementing SVM on different data sets

Learning mathematics behind SVM (optional)

and much, much more!

Enroll in the course and become a data scientist today!

Who this course is for:

• This course is for you if you want to learn how to program in Python for Machine Learning

• This course is for you if you want to make a predictive analysis model

• This course is for you if you are tired of Machine Learning courses that are too complicated and expensive

• This course is for you if you want to learn Python by doing

SVM using Scikit-Learn, SVM using NumPy, Implementing of Support Vector Machine or SVM on different datasets

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What you will learn

• Learn the basics of Machine Learning
• Learn basics of Discriminative Learning
• Learn basics of Linear Discriminants

Rating: 5

Level: All Levels

Duration: 11.5 hours

Instructor: AI Sciences