Software Alternatives & Reviews

monkeylearn VS Amazon Machine Learning

Compare monkeylearn VS Amazon Machine Learning and see what are their differences

monkeylearn logo monkeylearn

Text Mining Made Easy. Extract and classify information from text. Integrate with your App within minutes.

Amazon Machine Learning logo Amazon Machine Learning

Machine learning made easy for developers of any skill level
  • monkeylearn Landing page
    Landing page //
    2022-04-02
  • Amazon Machine Learning Landing page
    Landing page //
    2023-03-13

monkeylearn videos

Analyzing Customer Reviews with MonkeyLearn and RapidMiner

More videos:

  • Review - Webinar - Introduction to MonkeyLearn
  • Review - Making a Custom Text Classifier with MonkeyLearn

Amazon Machine Learning videos

Introduction to Amazon Machine Learning - Predictive Analytics on AWS

More videos:

  • Tutorial - AWS Machine Learning Tutorial | Amazon Machine Learning | AWS Training | Edureka

Category Popularity

0-100% (relative to monkeylearn and Amazon Machine Learning)
NLP And Text Analytics
100 100%
0% 0
AI
0 0%
100% 100
Natural Language Processing
Developer Tools
0 0%
100% 100

User comments

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Social recommendations and mentions

Based on our record, monkeylearn should be more popular than Amazon Machine Learning. It has been mentiond 11 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

monkeylearn mentions (11)

  • Best AI SEO Tools for NLP Content Optimization
    MonkeyLearn: A platform for text analysis and machine learning, allowing users to train custom models for tasks like sentiment analysis and topic classification. Source: 5 months ago
  • free-for.dev
    Monkeylearn.com — Text analysis with machine learning, free 300 queries/month. - Source: dev.to / over 1 year ago
  • [D] What are the best SaaS APIs for non-English NLP tasks?
    MonkeyLearn supports 11 languages for data analysis (Spanish, Portuguese, German, Russian, Italian, French, Dutch, Chinese, Japanese, Korean and Arabic).  But for sentiment analysis, only Spanish seems to be available, I’m not sure about that. Source: over 1 year ago
  • Word Cloud From This Sub [OC]
    R3: Used RedditExtractoR in R to download all-time top posts, and ran the resulting .csv through https://monkeylearn.com/. Downloaded the resulting table and deleted top result "OC" - then visualized it with ggplot to give a sense of absolute numbers. Total posts considered in this are 988, the word cloud only looks at the 98 most mentioned words/phrases. Let me know if you have got any questions/concerns! Source: almost 2 years ago
  • Can I use Google Search Console to look at queries and improve my blog posts?
    Go to monkeylearn.com and sign up for a free demo. Then cut and paste your blog text into the extractor/classifier. Source: almost 2 years ago
View more

Amazon Machine Learning mentions (2)

  • Rant + Planning to learn full stack development
    There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: over 1 year ago
  • Ask the Experts: AWS Data Science and ML Experts - Mar 9th @ 8AM ET / 1PM GMT!
    Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: about 3 years ago

What are some alternatives?

When comparing monkeylearn and Amazon Machine Learning, you can also consider the following products

Amazon Comprehend - Discover insights and relationships in text

Machine Learning Playground - Breathtaking visuals for learning ML techniques.

spaCy - spaCy is a library for advanced natural language processing in Python and Cython.

Lobe - Visual tool for building custom deep learning models

Google Cloud Natural Language API - Natural language API using Google machine learning

Apple Machine Learning Journal - A blog written by Apple engineers