Search results for %s

Found 31 documents, 5934 searched:

• Getting Started With Go Programing For Data Science

  Learn how to perform data analysis, data visualization, and model training in GoLang, just like Python.

  https://www.kdnuggets.com/getting-started-with-go-programing-for-data-science

• Python f-Strings Magic: 5 Game-Changing Tricks Every Coder Needs to Know

  Let’s explore some of the lesser known but super helpful uses of Python f-strings in debugging, formatting dates and LLM prompt templates, and more.

  https://www.kdnuggets.com/python-fstrings-magic-5-gamechanging-tricks-every-coder-needs-to-know

• Deploying Your Machine Learning Model to Production in the Cloud

  Learn a simple way to have a live model hosted on AWS.

  https://www.kdnuggets.com/deploying-your-ml-model-to-production-in-the-cloud

• How to Optimize SQL Queries for Faster Data Retrieval

  Today, we’ll talk about why SQL query optimization is important and which techniques can be used to optimize it.

  https://www.kdnuggets.com/2023/06/optimize-sql-queries-faster-data-retrieval.html

• 10 Jupyter Notebook Tips and Tricks for Data Scientists

  Unlock the full potential of Jupyter Notebook with expert tips and techniques, including time-saving shortcuts, powerful magic functions, and advanced features, to boost your productivity.

  https://www.kdnuggets.com/2023/06/10-jupyter-notebook-tips-tricks-data-scientists.html

• Schedule & Run ETLs with Jupysql and GitHub Actions

  This blog provided you with a comprehensive overview of ETL and JupySQL, including a brief introduction to ETLs and JupySQL. We also demonstrated how to schedule an example ETL notebook via GitHub actions, which allows you to automate the process of executing ETLs and JupySQL from Jupyter.

  https://www.kdnuggets.com/2023/05/schedule-run-etls-jupysql-github-actions.html

• Text Preprocessing Methods for Deep Learning

  While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.

  https://www.kdnuggets.com/2021/09/text-preprocessing-methods-deep-learning.html

• A Python Data Processing Script Template

  Here's a skeleton general purpose template for getting a Python command line script fleshed out as quickly as possible.

  https://www.kdnuggets.com/2021/08/python-data-processing-script-template.html

• Are You Still Using Pandas to Process Big Data in 2021? Here are two better options">Are You Still Using Pandas to Process Big Data in 2021? Here are two better options

  When its time to handle a lot of data -- so much that you are in the realm of Big Data -- what tools can you use to wrangle the data, especially in a notebook environment? Pandas doesn’t handle really Big Data very well, but two other libraries do. So, which one is better and faster?

  https://www.kdnuggets.com/2021/03/pandas-big-data-better-options.html

• Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak

  If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.

  https://www.kdnuggets.com/2020/10/deploying-secure-scalable-streamlit-apps-aws-docker-swarm-traefik-keycloak.html

• Making Python Programs Blazingly Fast

  Let’s look at the performance of our Python programs and see how to make them up to 30% faster!

  https://www.kdnuggets.com/2020/09/making-python-programs-blazingly-fast.html

• Generate Realistic Human Face using GAN

  This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator.

  https://www.kdnuggets.com/2020/03/generate-realistic-human-face-using-gan.html

• Intent Recognition with BERT using Keras and TensorFlow 2

  TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!

  https://www.kdnuggets.com/2020/02/intent-recognition-bert-keras-tensorflow.html

• Stock Market Forecasting Using Time Series Analysis

  Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.

  https://www.kdnuggets.com/2020/01/stock-market-forecasting-time-series-analysis.html

• Predict Electricity Consumption Using Time Series Analysis">Predict Electricity Consumption Using Time Series Analysis

  Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.

  https://www.kdnuggets.com/2020/01/predict-electricity-consumption-time-series-analysis.html

• Build Your First Voice Assistant

  Hone your practical speech recognition application skills with this overview of building a voice assistant using Python.

  https://www.kdnuggets.com/2019/09/build-your-first-voice-assistant.html

• Predict Age and Gender Using Convolutional Neural Network and OpenCV">Predict Age and Gender Using Convolutional Neural Network and OpenCV

  Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.

  https://www.kdnuggets.com/2019/04/predict-age-gender-using-convolutional-neural-network-opencv.html

• Deploy your PyTorch model to Production

  This tutorial aims to teach you how to deploy your recently trained model in PyTorch as an API using Python.

  https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html

• Simple Yet Practical Data Cleaning Codes

  Real world data is messy and needs to be cleaned before it can be used for analysis. Industry experts say the data preprocessing step can easily take 70% to 80% of a data scientist's time on a project.

  https://www.kdnuggets.com/2019/02/simple-yet-practical-data-cleaning-codes.html

• Introduction to Named Entity Recognition

  Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.

  https://www.kdnuggets.com/2018/12/introduction-named-entity-recognition.html

• Multi-Class Text Classification with Doc2Vec & Logistic Regression

  Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.

  https://www.kdnuggets.com/2018/11/multi-class-text-classification-doc2vec-logistic-regression.html

• Multi-Class Text Classification Model Comparison and Selection

  This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.

  https://www.kdnuggets.com/2018/11/multi-class-text-classification-model-comparison-selection.html

• Named Entity Recognition and Classification with Scikit-Learn">Named Entity Recognition and Classification with Scikit-Learn

  Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically.

  https://www.kdnuggets.com/2018/10/named-entity-recognition-classification-scikit-learn.html

• An End-to-End Project on Time Series Analysis and Forecasting with Python

  Time series are widely used for non-stationary data, like economic, weather, stock price, and retail sales in this post. We will demonstrate different approaches for forecasting retail sales time series.

  https://www.kdnuggets.com/2018/09/end-to-end-project-time-series-analysis-forecasting-python.html

• IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

  I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  https://www.kdnuggets.com/2018/06/zimbres-iot-aws-machine-learning-dashboard.html

• Jupyter Notebook for Beginners: A Tutorial

  The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.

  https://www.kdnuggets.com/2018/05/jupyter-notebook-beginners-tutorial.html

• Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences

  Part 4 of this series employs some of the lessons learned thus far to analyze tweets related to rugby matches and term co-occurrences.

  https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-4.html

• Mining Twitter Data with Python Part 1: Collecting Data

  Part 1 of a 7 part series focusing on mining Twitter data for a variety of use cases. This first post lays the groundwork, and focuses on data collection.

  https://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-1.html

• R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

  R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.

  https://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

• Doing Data Science: A Kaggle Walkthrough – Cleaning Data

  Gain insight into the process of cleaning data for a specific Kaggle competition, including a step by step overview.

  https://www.kdnuggets.com/2016/03/doing-data-science-kaggle-walkthrough-cleaning-data.html

• Getting started with Python and Apache Flink

  Apache Flink built on top of the distributed streaming dataflow architecture, which helps to crunch massive velocity and volume data sets. With version 1.0 it provided python API, learn how to write a simple Flink application in python.

  https://www.kdnuggets.com/2015/11/getting-started-python-apache-flink.html