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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. NLTK vs PredictionIO

NLTK vs PredictionIO

OverviewComparisonAlternatives

Overview

PredictionIO
PredictionIO
Stacks67
Followers110
Votes8
NLTK
NLTK
Stacks136
Followers179
Votes0

NLTK vs PredictionIO: What are the differences?

NLTK: It is a leading platform for building Python programs to work with human language data. It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language; PredictionIO: Open Source Machine Learning Server. PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

NLTK and PredictionIO can be primarily classified as "Machine Learning" tools.

PredictionIO is an open source tool with 12K GitHub stars and 1.95K GitHub forks. Here's a link to PredictionIO's open source repository on GitHub.

According to the StackShare community, PredictionIO has a broader approval, being mentioned in 10 company stacks & 32 developers stacks; compared to NLTK, which is listed in 15 company stacks and 17 developer stacks.

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Detailed Comparison

PredictionIO
PredictionIO
NLTK
NLTK

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process.
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Statistics
Stacks
67
Stacks
136
Followers
110
Followers
179
Votes
8
Votes
0
Pros & Cons
Pros
  • 8
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What are some alternatives to PredictionIO, NLTK?

TensorFlow

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

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