Author name: Vili Merilainen

How to Encode Categorical Values for Multiple Columns | Scikit-Learn

Label encoding is a data preprocessing technique used in machine learning to convert categorical values into numerical form, facilitating their use in algorithms that require numerical input. In label encoding, each category is assigned a unique integer based on alphabetical ordering, frequency, or any other criteria deemed appropriate for the task at hand. For instance, …

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Sketch of Flash exhibiting his renowned speed, with sparks of energy around him, moving in tandem with vibrant data currents. These currents feed into a GPU with two clearly visible fans in motion. Overhead, a luminous cloud bearing a neural network icon underscores rapid training.

Can scikit-learn Utilize My GPU?

Utilizing GPUs in machine learning is highly advantageous because they are designed to handle multiple operations simultaneously, offering a substantial boost in computational power, especially for tasks that can be parallelized. Machine learning models, particularly deep learning networks, involve a multitude of matrix multiplications and other calculations that are inherently parallelizable. A GPU, with its …

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Cartoon drawing of a cute panda sitting on the floor with a big book titled 'Datasets'. The panda is flipping through the pages, and above its head are dreamy bubbles filled with graphs, pie charts, and bar diagrams.

How to Convert a Scikit-Learn Dataset to a Pandas DataFrame

By reading this article, you will learn the best way to convert a scikit-learn dataset to a pandas DataFrame object in Python. If you only want the quick 10 second instructions, here they are: Scikit-learn, often referred to as sklearn, is an open-source machine learning library in Python that provides simple and efficient tools for …

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Photo of a futuristic office desk in a dimly lit room. Dominating the scene is a sleek monitor that takes up more than 50% of the frame. The monitor displays a brightly glowing bar, separated into two parts by a line. The larger section is a distinct color and is labeled 'Train', while the smaller section has a different color and is labeled 'Test'. The ambient light from the monitor casts a soft glow on its surroundings, causing the surface of the desk to reflect its light.

How to Split a Pandas Dataframe Randomly into Train and Test Sets with Scikit-Learn and Python

By reading this article, you will learn how to use the train_test_split() -function from the scikit-learn library to split your Pandas Dataframe dataset into train and test sets. The training set can then be utilized for training machine learning models, and the test set can be used to test the performance/accuracy of the resulting model. …

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Save and Load Models to Disk in Keras, Python: A Complete Guide

In machine/deep learning, it’s essential to save your trained models so you can reuse, share, or deploy them without wasting time and computational resources to retrain them from scratch. In Keras, one of the most popular deep-learning libraries, this process is both straightforward and versatile. This article will guide you through the process of saving …

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Save and Load Models to Disk in PyTorch, Python: A Complete Guide

Learn the essentials of saving and loading models in PyTorch with our complete guide. Dive into the details of handling the state_dict and understand its pivotal role in managing model parameters, vital for tasks like transfer learning and model sharing. Grasp practical insights into saving classifiers for inference, ensuring a smooth transition from training to …

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Save and Load Models on Disk in Scikit-Learn, Python: A Complete Guide

Learn the necessities of model management with our guide. This article navigates through the methodical waters of saving your trained Scikit-Learn classifiers using two separate libraries: Pickle and Joblib. Anticipate a deep dive into the pros, cons, and how-tos of each method, ensuring your models are not only securely stored but also readily accessible for …

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An example image from each class from the MNIST Digits dataset

Applications of Machine Learning – A Glossary of 20+ Terms

Discover the diverse ways Machine Learning (ML) and Artificial Intelligence (AI) are applied to real-world problems! This comprehensive glossary features more than 20 applications that are changing the way the world works! Whether you’re an AI enthusiast or just beginning to venture into the realm of Machine Learning, this glossary serves as a valuable resource …

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Model Validation and Performance Evaluation in ML – 30 + terms you should know

Embark on a journey to master Model Validation and Performance Evaluation in Machine Learning with our extensive glossary, featuring over 30 crucial terms you should know! Whether you’re an AI connoisseur or just starting to explore the world of Machine Learning (ML), this glossary is an indispensable resource for broadening your understanding and deepening your …

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