Stack Overflow
stackoverflow.com › questions › 44849324 › machine-learning-with-json-dataset
python - Machine Learning with JSON Dataset - Stack Overflow
You have tagged scikit-learn, so I assume you want to work in it. No, there is no way in scikit to work directly with KV pairs. There is DictVectorizer available which will convert your data to appropriate one ... If you don't insist on using python, you can use the following Julia libraries which are specifically suited for ML on JSONs: github.com/pevnak/Mill.jl and github.com/pevnak/JsonGrinder.jl
GitHub
github.com › jdorfman › awesome-json-datasets
GitHub - jdorfman/awesome-json-datasets: A curated list of awesome JSON datasets that don't require authentication. · GitHub
A curated list of awesome JSON datasets that don't require authentication. - jdorfman/awesome-json-datasets
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Languages JavaScript
Videos
08:44
Save Neural Network in JSON file | Save Neural Network model | ...
15:51
Twitter data extracted from JSON | Python Machine Learning | How ...
01:02:26
Kaggle Live-coding: Reading in JSON Files | Kaggle - YouTube
Normalize JSON Dataset With pandas
18:10
Learn JSON in 1 video (real-world examples and critical tools ...
12:46
Working with JSON Data: A Real Example PART 1 | #195 - YouTube
DigitalOcean
digitalocean.com › community › tutorials › json-for-finetuning-machine-learning-models
How to Use JSON for Fine-Tuning Machine Learning Models | DigitalOcean
March 25, 2025 - Experience with Python and ML Frameworks – Proficiency in Python, along with libraries like TensorFlow, PyTorch, or Scikit-learn. Data Preprocessing Skills – Ability to clean, format, and convert JSON datasets for ML training. GPU/Cloud Setup (Optional) – For large-scale fine-tuning, access to cloud GPUs (e.g., DigitalOcean GPU Droplets) can speed up the process. Storing Hyperparameters for Fine-tuning – Hyperparameters control the training behavior of a model. Instead of hardcoding them in scripts, JSON files allow easy modification and reproducibility.
egghead.io
egghead.io › lessons › javascript-classify-json-text-data-with-machine-learning-in-natural
Classify JSON text data with machine learning in Natural | egghead.io
In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories. While this dataset is still considered a small dataset -- only a couple hundred points of data -- ...
Published November 16, 2016
Kaggle
kaggle.com › questions-and-answers › 218530
Neural networks or deep learning with json dataset | Kaggle
Neural networks or deep learning with json dataset
Kaggle
kaggle.com › datasets
Find Open Datasets and Machine Learning Projects
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Kaggle
kaggle.com › datasets › rtatman › iris-dataset-json-version
Iris Dataset (JSON Version)
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Dadroit
dadroit.com › blog › json-datasets
List of Open Datasets That Can Be Used As JSON Sample Data
September 25, 2023 - The API offers the video game information on the main character's game plays. Dstl This data provider gathers a standard dataset that could be used to train and validate machine learning strategies and NLP methods.
Naftaliharris
naftaliharris.com › blog › machine-learning-json
Machine Learning over JSON
With existing machine learning algorithms, you have to coerce the JSON document into the vector representation with your featurization. Essentially you do this by flattening and imputing.
V7 Labs
v7labs.com › home › blog › 65+ best free datasets for machine learning
65+ Best Free Datasets for Machine Learning [2024 Update]
There are three versions available: original, sorted by dates, and with removed duplicates. This dataset is commonly used for experiments in text applications of machine learning techniques, ...
Ndjson
ndjson.com › home › use cases › machine learning
JSONL for Machine Learning - Training Data & Model Inputs
January 1, 2024 - Hugging Face Datasets library has native JSONL support with powerful features. from datasets import load_dataset # Load single JSONL file dataset = load_dataset('json', data_files='train.jsonl') # Load train/validation/test splits dataset = load_dataset('json', data_files={ 'train': 'train.jsonl', 'validation': 'val.jsonl', 'test': 'test.jsonl' }) # Load from multiple files with wildcards dataset = load_dataset('json', data_files='data/*.jsonl') # Stream large datasets without downloading entirely dataset = load_dataset('json', data_files='huge_file.jsonl', streaming=True) # Access data print(dataset['train'][0]) print(f"Training examples: {len(dataset['train'])}")
WebDataRocks
webdatarocks.com › home › blog › top public dataset sources for data analysis and machine learning
Top Public Dataset Sources for Data Analysis and Machine Learning • WebDataRocks
October 28, 2024 - What is more, all the datasets are categorized by use of machine learning algorithms, which makes this platform even more intriguing. Try digging deeper to find here the most challenging datasets for your work. Developers may find useful the fact that Socrata OpenData exposes the Discovery API which presents a mighty way for getting access to all the public data from the platform. Another great feature for developers is the fact that API calls return nested JSON objects which are easy to understand and parse.
Medium
medium.com › analytics-vidhya › extract-the-useful-data-from-jason-file-for-data-sceince-34ed5ae0b350
Extract The Useful Data from JSON File For Machine Learning | by Ankit Gupta | Analytics Vidhya | Medium
July 29, 2019 - How to extract data from a JSON file for your machine learning model in Python
DataHub
datahub.io › blog › machine-learning-datasets
Machine learning datasets
You can see our current machine-learning datasets at https://datahub.io/machine-learning · Machine learning is the science of making computers learn like humans do and to also improve their capabilities to learn to act without being explicitly programmed. It is used as a general term for ...
Superjson
superjson.ai › home › blog › jsonl in machine learning: the complete guide to json lines for ai training data and datasets
JSONL in Machine Learning: The Complete Guide to JSON Lines for AI Training Data and Datasets
September 7, 2025 - Master JSONL format for machine learning workflows. Learn how to use JSON Lines for OpenAI fine-tuning, AI training datasets, streaming data pipelines, and ML frameworks integration.
SQL Shack
sqlshack.com › how-to-use-json-data-in-azure-machine-learning
How to use JSON data in Azure Machine Learning
November 22, 2019 - The Import data module supports the following data sources but this list does not include any provider for JSON data. ... Microsoft recommends that if we need to import data from JSON we can use Execute Python Script or Execute R Script modules. In this article, we will use Execute R Script module. In the following demonstrations, we will use Execute R Script module. This module is used to execute R script codes in Azure ML Studio. The Execute R Script module has three input parameters. These are Dataset1, Dataset2 and Script Bundle.
Ultralytics
ultralytics.com › glossary › json
JSON: Data Format for AI & Machine Learning
The popular COCO dataset format uses a comprehensive JSON schema to define image file paths, license information, and annotation coordinates. This contrasts with other formats like the YOLO TXT format, which uses simple space-separated text files.
Kaggle
kaggle.com › datasets › karthikrathod › json-files-of-datasets
json files of datasets
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Ic
intro2ml.pages.doc.ic.ac.uk › autumn2021 › modules › lab-java › json
Handling JSON files | COMP70050: Introduction to Machine Learning | Department of Computing | Imperial College London
import json data = { "course": { "name": "Introduction to Machine Learning", "term": 1 } } with open("data.json", "w") as f: json.dump(data, f)