Python-tutorial

Learn the common tricks to handle categorical data and preprocess it to build machine learning models!

In this basic Python tutorial, you'll learn about why and when you should use inner classes.

Discover how to write reusable and efficient Python functions. Master parameters, return statements, and advanced topics like lambda functions. Organize your code better with main() and other best practices.

Learn about the basics of feature selection and how to implement and investigate various feature selection techniques in Python.

Learn about PCA and how it can be leveraged to extract information from the data without any supervision using two popular datasets: Breast Cancer and CIFAR-10.

Discover Long Short-Term Memory (LSTM) networks in Python and how you can use them to make stock market predictions!

Learn about Markov Chains, their properties, transition matrices, and implement one yourself in Python!

In this tutorial, you'll learn about and how to code in Python the probability distributions commonly referenced in machine learning literature.

In this tutorial, you'll learn about Support Vector Machines, one of the most popular and widely used supervised machine learning algorithms.

This tutorial will demonstrate how you can install Anaconda, a powerful package manager, on Microsoft Windows.

Learn about logistic regression, its basic properties, and build a machine learning model on a real-world application in Python using scikit-learn.

Python NLTK (natural language toolkit) sentiment analysis tutorial. Learn how to create and develop sentiment analysis using Python. Follow specific steps to mine and analyze text for natural language processing.