Linear Classifiers in Python

Notes from the relevant course from Datacamp. In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn.

Prerequisites - How to install Python

Based on the Operating System you are running on, you can follow the instructions on the official Python website.

An easy solution is to use Python Virtual Environments. This way you can have multiple Python versions installed on your machine and you can create isolated environments for each project. In order to do so, you can run the following commands:

python -m venv venv

This will create a virtual environment in the venv directory. You can activate it by running:

For Linux

source venv/bin/activate

For Windows

.\venv\Scripts\activate.bat # or ./venv/Scripts/activate.bat (depending on your terminal configuration)

Another intuitive solution is to use Anaconda which is a Python distribution that comes with a lot of useful packages and tools.

How to install the project dependencies

After you have activated the Python environment, you can install the dependencies by running:

pip install -r requirements.txt

This command will install all the dependencies listed in the requirements.txt file.

Course Description

Linear classifiers are amongst the most practical and widely used machine learning models. Learn about the most effective machine learning techniques, and gain practice implementing them in Python. You'll learn about the differences between logistic regression and linear discriminant analysis, and about linear classifiers more broadly. You'll also practice on real-world datasets, and build a foundation on which you can use and understand more complex machine learning models.

In this course you'll learn all about using linear classifiers, specifically logistic regression and support vector machines, with scikit-learn. Once you've learned how to apply these methods, you'll dive into the ideas behind them and find out what really makes them tick. At the end of this course you'll know how to train, test, and tune these linear classifiers in Python. You'll also have a conceptual foundation for understanding many other machine learning algorithms.

Course Chapters

Chapter 1: Applying logistic regression and SVM

In this chapter you will learn the basics of applying logistic regression and support vector machines (SVMs) to classification problems. You'll use the scikit-learn library to fit classification models to real data.

Chapter 1 Details

• ΚΝΝ classification