NLP Cloud
spaCy
Amazon Comprehend
Google Cloud Natural Language API
Polyglot NLP
TextBlob
OpenNLP
Hugging Face
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
Exploratory
htm.java
NLP Cloud serves high performance pre-trained or custom models for NER, sentiment-analysis, classification, summarization, dialogue summarization, paraphrasing, intent classification, product description and ad generation, chatbot, grammar and spelling correction, keywords and keyphrases extraction, text generation, question answering, machine translation, language detection, semantic similarity, tokenization, POS tagging, embeddings, and dependency parsing. It is ready for production, served through a REST API.
You can either use the NLP Cloud pre-trained models, fine-tune your own models, or deploy your own models.
NLP Cloud Scikit-learnNo NLP Cloud videos yet. You could help us improve this page by suggesting one.
NLP Cloud might be a bit more popular than Scikit-learn. We know about 41 links to it since March 2021 and only 28 links to Scikit-learn. 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.
NLP Cloud (their Dolphin and Fine-tuned GPT-NeoX models). Source: 11 months ago
I am using NLP Cloud more and more and have not seen such quality drop with their service. Source: 11 months ago
You have NLP Cloud which is a nice and comprehensive OpenAI competitor. Source: 12 months ago
You should try NLP Cloud, they don't censor their text generation models: https://nlpcloud.com/home/playground. Source: 12 months ago
You can use NLP Cloud, as far as I know they don't ban anybody and don't filter NSFW. Source: 12 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 3 months ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 12 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: over 1 year ago
spaCy - spaCy is a library for advanced natural language processing in Python and Cython.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Amazon Comprehend - Discover insights and relationships in text
OpenCV - OpenCV is the world's biggest computer vision library
Google Cloud Natural Language API - Natural language API using Google machine learning
NumPy - NumPy is the fundamental package for scientific computing with Python