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Machine Learning Tutorial
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K-Nearest Neighbors (KNN) Classification with scikit-learn
This article covers how and when to use k-nearest neighbors classification with scikit-learn. Focusing on concepts, workflow, and examples. We also cover distance metrics and how to select the best value for k using cross-validation.
Adam Shafi
February 20, 2023
Decision Tree Classification in Python Tutorial
In this tutorial, learn Decision Tree Classification, attribute selection measures, and how to build and optimize Decision Tree Classifier using Python Scikit-learn package.
Avinash Navlani
June 27, 2024
A Complete Guide to Data Augmentation
Learn about data augmentation techniques, applications, and tools with a TensorFlow and Keras tutorial.
Abid Ali Awan
December 9, 2024
All posts
Proximal Policy Optimization with PyTorch and Gymnasium
Learn the first principles of Proximal Policy Optimization, including its implementation in PyTorch with Gymnasium!
Arun Nanda
November 18, 2024
Machine Learning with Python & Snowflake Cortex AI: A Guide
Learn about Snowflake Cortex AI and how it can be used for LLMs and machine learning.
Austin Chia
November 8, 2024
The A* Algorithm: A Complete Guide
A guide to understanding and implementing the A* search algorithm in Python. See how to create efficient solutions for complex search problems with practical code examples. Learn optimization strategies used in production environments.
Rajesh Kumar
November 7, 2024
Introduction to Podman for Machine Learning: Streamlining MLOps Workflows
A lightweight, daemonless Docker Desktop alternative that streamlines container management, enabling fast training, evaluation, and deployment of machine learning models.
Abid Ali Awan
November 6, 2024
Understanding the Bellman Equation in Reinforcement Learning
The Bellman Equation is a key concept in reinforcement learning that helps agents make decisions in complex situations by assessing possible future states and rewards. This article examines its mathematical principles, real-world uses, and importance in creating optimal policies within Markov Decision Processes.
Kurtis Pykes
November 6, 2024
US Election 2024 Prediction With Machine Learning and Python
Learn how to predict the winner of the 2024 US presidential election using Python, machine learning, and data from FiveThirtyEight and the Federal Election Commission.
Tom Farnschläder
October 30, 2024
RMSprop Optimizer Tutorial: Intuition and Implementation in Python
Learn about the RMSprop optimization algorithm, its intuition, and how to implement it in Python. Discover how this adaptive learning rate method improves on traditional gradient descent for machine learning tasks.
Bex Tuychiev
October 23, 2024
How to Visualize Machine Learning Models: From Linear Regression to Neural Networks
Machine learning is complex and often hard to wrap your head around. By visualizing machine learning models, you can get a great level of understanding of model performance and the decisions the model makes when making predictions.
Dario Radečić
October 23, 2024
A Guide to the DBSCAN Clustering Algorithm
Learn how to implement DBSCAN, understand its key parameters, and discover when to leverage its unique strengths in your data science projects.
Rajesh Kumar
September 29, 2024
Adagrad Optimizer Explained: How It Works, Implementation, & Comparisons
Learn the Adagrad optimization technique, including its key benefits, limitations, implementation in PyTorch, and use cases for optimizing machine learning models.
Satyam Tripathi
September 26, 2024
Isolation Forest Guide: Explanation and Python Implementation
Isolation Forest is an unsupervised machine learning algorithm that identifies anomalies or outliers in data by isolating them through a process of random partitioning within a collection of decision trees.
Conor O'Sullivan
September 25, 2024
SARSA Reinforcement Learning Algorithm in Python: A Full Guide
Learn SARSA, an on-policy reinforcement learning algorithm. Understand its update rule, hyperparameters, and differences from Q-learning with practical Python examples and its implementation.
Bex Tuychiev
September 19, 2024