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

Description

In this course you will learn how to use the power of Python to train your machine such that your machine starts learning just like human and based on that learning, your machine starts making predictions as well!

This course is truly a step by step. In every new video, we build on what has already been learned and move one extra step forward; then we assign you a small task that is solved in the beginning of the next video. 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 a human; based on that learning, your machine starts making predictions as well! We'll be using Python as the programming language in this course, which is the hottest language nowadays when 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 the general concepts of machine learning, 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 implemented using Python. All code files and resources are placed here: https://github.com/PacktPublishing/Machine-Learning-A-Z-Support-Vector-Machine-with-Python-

Learn the basics of machine learning
Learn the basics of discriminative learning
Learn the basics of linear discriminants
Learn the basics of Support Vector Machine (SVM)
Learn the basics of the sparsity of SVM and comparison with logistic regression
Learn how to implement SVM on any dataset
Learn the math behind SVM

This course is designed for both beginners with some programming experience or even those who know nothing about ML and SVM.
This course is for someone who is curious to learn the math behind SVM since this course contains an optional part for mathematics as well.
It is also for someone who wants to learn logistic regression from zero to hero; for someone who is an absolute beginner and has very little idea of machine learning.

This course covers both the theory and the practical aspects of SVM and machine learning. You are first introduced to the theoretical part of the concept and then we implement everything practically using Python.

Learn how to use Pandas for data analysis * Learn how to use sci-kit-learn for SVM using the Titanic dataset * Learn about training data, testing data, and outliers

https://github.com/PacktPublishing/Machine-Learning-A-Z-Support-Vector-Machine-with-Python-

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM. AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences. Their courses have successfully helped more than 100,000 students master AI and data science.

1. Introduction to course

2. Introduction to Machine Learning

3. Introduction to Python

4. Support Vector Machine

5. Optional SVM Section

6. Bonus Lecture