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Machine Learning Tutorial
Get insights & best practices into AI & machine learning, upskill, and build data cultures. Learn how to get the most out of machine learning models with our tutorials.
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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
November 23, 2022
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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
Optimization in Python: Techniques, Packages, and Best Practices
This article teaches you about numerical optimization, highlighting different techniques. It discusses Python packages such as SciPy, CVXPY, and Pyomo and provides a practical DataLab notebook to run code examples.
Kurtis Pykes
August 31, 2024
Adam Optimizer Tutorial: Intuition and Implementation in Python
Understand and implement the Adam optimizer in Python. Learn the intuition, math, and practical applications in machine learning with PyTorch
Bex Tuychiev
August 29, 2024
What is Boosting?
Boosting improves machine learning performance by sequentially correcting errors and combining weak learners into strong predictors.
Vinod Chugani
August 16, 2024
Optuna for Deep Reinforcement Learning in Python
Explore how to master hyperparameter tuning with Optuna. Learn how to define hyperparameters, set up your objective function, and utilize sampling and pruning techniques in deep reinforcement learning.
Bunmi Akinremi
August 7, 2024
DeepChecks Tutorial: Automating Machine Learning Testing
Learn how to perform data and model validation to ensure robust machine learning performance using our step-by-step guide to automating testing with DeepChecks.
Abid Ali Awan
August 6, 2024
Grafana Tutorial: A Beginner’s Guide to Monitoring Machine Learning Models
Learn how to build a machine learning model monitoring system using Grafana, Prometheus, Flask, and Docker.
Bex Tuychiev
August 1, 2024
Stochastic Gradient Descent in Python: A Complete Guide for ML Optimization
Learn Stochastic Gradient Descent, an essential optimization technique for machine learning, with this comprehensive Python guide. Perfect for beginners and experts.
Bex Tuychiev
July 24, 2024
The Complete Guide to Machine Learning on AWS with Amazon SageMaker
This comprehensive tutorial teaches you how to use AWS SageMaker to build, train, and deploy machine learning models. We guide you through the complete workflow, from setting up your AWS environment and creating a SageMaker notebook instance to preparing data, training models, and deploying them as endpoints.
Bex Tuychiev
June 19, 2024