Tag: Jupyter (44)

• 3 More Google Colab Environment Management Tips - Jan 2, 2019.
  
  This is a short collection of lessons learned using Colab as my main coding learning environment for the past few months. Some tricks are Colab specific, others as general Jupyter tips, and still more are filesystem related, but all have proven useful for me.

  Tags: CoLaboratory, Google, Jupyter, Machine Learning, Python

• Here are the most popular Python IDEs / Editors - Dec 7, 2018.
  
  We report on the most popular IDE and Editors, based on our poll. Jupyter is the favorite across all regions and employment types, but there is competition for no. 2 and no. 3 spots.

  Tags: Jupyter, PyCharm, Python

• Best Machine Learning languages, Data Visualization Tools, DL Frameworks, and Big Data Tools - Dec 3, 2018.
  
  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  Tags: Big Data, Data Visualization, Deep Learning, Jupyter, Machine Learning, Python, R, Tableau

• What is the Best Python IDE for Data Science? - Nov 14, 2018.
  
  Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.

  Tags: Data Science, Jupyter, Programming, Python

• [Download] Real-Life ML Examples + Notebooks - Nov 13, 2018.
  
  In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!

  Tags: Databricks, ebook, Jupyter, Machine Learning, Use Cases

• Best Practices for Using Notebooks for Data Science - Nov 8, 2018.
  
  Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.

  Tags: Best Practices, Data Science, Jupyter

• How to Set Up a Free Data Science Environment on Google Cloud - Aug 15, 2018.
  
  In this post, we'll walk through how to set up a data science environment on Google Cloud Platform (GCP). Because of the economy of scale that cloud hosting companies provide, individuals or teams can affordably access powerful computers.

  Tags: Cloud, Data Science, Google, Google Cloud, Jupyter

• Data Scientist guide for getting started with Docker - Aug 14, 2018.
  
  Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started.

  Tags: Data Science, Data Scientist, Docker, Jupyter

• KDnuggets™ News 18:n29, Aug 1: Building an Awesome Data Science Portfolio; Data Science + DevOps = Taming the Unicorn - Aug 1, 2018.
  
  Also: A Practitioner's Guide to Processing & Understanding Text: Data Retrieval with Web Scraping; Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks; Best Deal in the Galaxy? Win KDnuggets Free Pass to Strata Data Conference NYC

  Tags: Data Science, Data Scientist, DevOps, Jupyter, Portfolio, SQL, Unicorn, Web Scraping

• Remote Data Science: How to Send R and Python Execution to SQL Server from Jupyter Notebooks - Jul 27, 2018.
  
  Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around.

  Tags: Jupyter, Machine Learning, Microsoft, Python, R, SQL, SQL Server

• 5 Data Science Projects That Will Get You Hired in 2018 - Jun 26, 2018.
  
  A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.

  Tags: Data Preparation, Data Science, Data Visualization, Hiring, Jupyter, Machine Learning

• JupyterCon – Exclusive KDnuggets Offer - May 3, 2018.
  
  JupyterCon returns to New York August 21-24. Save an additional 20% on individual Gold, Silver and Bronze passes with the code KDN20 before May 18.

  Tags: Jupyter, New York, NY, O'Reilly

• Jupyter Notebook for Beginners: A Tutorial - May 1, 2018.
  
  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.

  Pages: 1 2

  Tags: Data Analysis, GitHub, Jupyter, Matplotlib, Python

• Creating a simple text classifier using Google CoLaboratory - Mar 15, 2018.
  
  Google CoLaboratory is Google’s latest contribution to AI, wherein users can code in Python using a Chrome browser in a Jupyter-like environment. In this article I have shared a method, and code, to create a simple binary text classifier using Scikit Learn within Google CoLaboratory environment.

  Tags: CoLaboratory, Google, Jupyter, Python, Text Classification

• Top 5 Best Jupyter Notebook Extensions - Mar 13, 2018.
  
  Check out these 5 Jupyter notebook extensions to help increase your productivity.

  Tags: Jupyter, Python

• 5 Things to Know About Machine Learning - Mar 7, 2018.
  
  This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.

  Tags: Accuracy, CoLaboratory, Data Preparation, Ensemble Methods, Jupyter, Machine Learning, Validation

• Data Science for Javascript Developers  - Mar 2, 2018.
  
  The goal of this tutorial is to open the door to data science programming using Javascript. This tutorial is intended for Javascript programmers without any data science experience.

  Tags: Data Science, Data Visualization, Javascript, Jupyter

• Jupyter Pop-up coming to Boston on March 21 - Feb 28, 2018.
  
  Attend a day-long exploration of Jupyter's best practices and practical use cases in business and industry.

  Tags: Boston, Jupyter, MA, Machine Learning, O'Reilly, Software Engineering

• Fast.ai Lesson 1 on Google Colab (Free GPU) - Feb 8, 2018.
  
  In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?

  Tags: CoLaboratory, Deep Learning, fast.ai, Google, GPU, Jupyter

• Top KDnuggets tweets, Jan 3-9: A collection of Jupyter notebooks NumPy, Pandas, matplotlib, basic #Python #MachineLearning - Jan 10, 2018.
  
  Artificial General Intelligence (AGI) in less than 50 years; Top KDnuggets tweets: 10 Free Must-Read Books for #MachineLearning and #DataScience; The Art of Learning #DataScience; Supercharging Visualization with Apache Arrow; Docker for #DataScience

  Tags: Jupyter, numpy, Pandas, Top tweets

• Introducing R-Brain: A New Data Science Platform - Oct 11, 2017.
  
  R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

  Tags: Data Science Platform, Docker, Jen Underwood, Jupyter, R, R-Brain

• Top KDnuggets tweets, Sep 27 – Oct 03: Introduction to #Blockchains & What It Means to #BigData; 7 More Steps to Mastering #MachineLearning With #Python - Oct 4, 2017.
  
  Also Jupyter Notebooks are Breathtakingly Featureless - Use Jupyter Lab; The 4 Types of Data #Analytics; Aspiring Data Scientists! Learn the basics with these 7 books.

  Tags: Blockchain, Books, Jupyter, TensorFlow, Top tweets

• From Notebooks to JupyterLab – The Evolution of Data Science IDEs - Aug 16, 2017.
  
  This live webinar (Aug 22) will discuss the impact that the notebook experience has had on data science, and how JupyterLab - the next generation data science IDE - has evolved from the classic notebooks.

  Tags: Continuum Analytics, Data Science, Data Science Tools, Jupyter

• JupyterCon – Collaborative Data Science, New York, August 22-25 - Jul 10, 2017.
  
  Bloomberg, Microsoft, Netflix and others found how Jupyter Notebook - the new front end for collaborative data science - make data a competitive advantage. Save an extra 20% with code PCKDNG.

  Tags: Collaborative, Data Science, Jupyter, New York City, NY, O'Reilly, Python

• Exploratory Data Analysis in Python - Jul 7, 2017.
  
  We view EDA very much like a tree: there is a basic series of steps you perform every time you perform EDA (the main trunk of the tree) but at each step, observations will lead you down other avenues (branches) of exploration by raising questions you want to answer or hypotheses you want to test.

  Tags: Data Analysis, Data Exploration, Data Preparation, Jupyter, Python, SVDS

• Getting Started with Python for Data Analysis - Jul 5, 2017.
  
  A guide for beginners to Python for getting started with data analysis.
   

  Tags: Beginners, Data Analysis, Jupyter, numpy, Python

• How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 3 - Jul 4, 2017.
  
  In this last post of the series, I describe how I used more powerful machine learning algorithms for the click prediction problem as well as the ensembling techniques that took me up to the 19th position on the leaderboard (top 2%)

  Tags: Feature Engineering, Jupyter, Kaggle, Machine Learning, Python

• How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 2 - Jun 27, 2017.
  
  In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.

  Pages: 1 2

  Tags: Feature Engineering, Jupyter, Kaggle, Machine Learning, Python

• How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I - Jun 8, 2017.
  
  As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!

  Tags: Apache Spark, Feature Engineering, Jupyter, Kaggle, Machine Learning, Python

• Data Science for Newbies: An Introductory Tutorial Series for Software Engineers - May 31, 2017.
  
  This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.

  Tags: Apache Spark, Data Science, Jupyter, Machine Learning, Pandas, Python, Reddit, Scala, SQL

• KDnuggets™ News 17:n13, Apr 5: What makes a great data scientist? Best R Packages for Machine Learning - Apr 5, 2017.
  
  Also Best R Packages for Machine Learning; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Learning; A Short Guide to Navigating the Jupyter Ecosystem.

  Tags: Data Science Skills, Jupyter, Machine Learning, Pandas, R

• A Short Guide to Navigating the Jupyter Ecosystem - Mar 31, 2017.
  
  This post presents a no-nonsense overview of the Jupyter ecosystem, and a few tips, tricks and concepts you may find useful for navigating it.

  Tags: Jupyter, Python, SVDS

• Top KDnuggets tweets, Mar 22-28: Big #DataScience: Expectation vs. Reality - Mar 29, 2017.
  
  Also A Gentle Introduction To Graph Theory; An Overview of #Python #DeepLearning Frameworks; The Great Algorithm Tutorial Roundup.

  Tags: Graph Theory, Jupyter, Python, Top tweets

• Moving from R to Python: The Libraries You Need to Know - Feb 24, 2017.
  
  Are you considering making a move from R to Python? Here are the libraries you need to know, how they stack up to their R contemporaries, and why you should learn them.

  Tags: Jupyter, Pandas, Programming, Python, R, scikit-learn, Yhat

• Jupyter Notebook Best Practices for Data Science - Oct 20, 2016.
  
  Check out this overview of Jupyter notebook best practices as pertains to data science. Novice or expert, you may find something of use here.

  Tags: Data Science, Jupyter, Python, SVDS

• Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all? - Aug 24, 2016.
  
  In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.

  Tags: Databases, Jupyter, Machine Learning, TensorFlow, Top tweets

• Visualizing 1 Billion Points of Data: Doing It Right – Aug 18 Webinar - Aug 11, 2016.
  
  Join Continuum Analytics on August 18 for a webinar on Big Data visualization with the datashader library. Save your spot today!

  Tags: Continuum Analytics, Data Visualization, Jupyter, Python

• Top KDnuggets tweets, Jul 13 – Jul 19: Bayesian #MachineLearning, Explained; Introducing JupyterLab - Jul 20, 2016.
  
  Bayesian #MachineLearning, Explained; JupyterLab: the next generation of the #Jupyter Notebook; On the importance of democratizing #ArtificialIntelligence

  Tags: Bayesian, Jupyter, Machine Learning, Python, Top tweets

• Statistical Data Analysis in Python - Jul 18, 2016.
  
  This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects, taking the form of a set of IPython notebooks.

  Tags: IPython, Jupyter, Pandas, Python, Statistical Analysis

• Top KDnuggets tweets, Jul 6 – Jul 12: Statistical Data Analysis #Python #Jupyter Notebooks; Modern Pandas Notebooks - Jul 13, 2016.
  
  Statistical Data Analysis in #Python (#Jupyter Notebooks); Modern Pandas: idiomatic Pandas notebook collection; New (free) book by @rdpeng: #rstats Programming for #DataScience

  Tags: Data Analysis, Jupyter, Pandas, Python, Statistical Analysis, Top tweets

• Jupyter+Spark+Mesos: An “Opinionated” Docker Image - May 31, 2016.
  
  Check "opinionated" Docker-based stacks for Jupyter, including one to combine Jupyter and Spark right out of the gate.

  Tags: Apache Spark, Docker, IBM, Jupyter

• R or Python? Consider learning both - Mar 8, 2016.
  
  The key to become a data science professional is in understanding the underlying data science concepts and work towards expanding your programming toolbox as much as you can. Hence, one should understand when to use Python and when to pick R, rather mastering just one language.

  Pages: 1 2

  Tags: DataCamp, Dataiku, Jupyter, Python, Python vs R, R

• Using Python and R together: 3 main approaches - Dec 10, 2015.
  
  Well if Data Science and Data Scientists can not decide on what data to choose to help them decide which language to use, here is an article to use BOTH.

  Tags: Ajay Ohri, Jupyter, Python, Python vs R, R

• Building and Sharing R packages made easy with Anaconda - Sep 15, 2015.
  
  "R Essentials" bundle comes with Jupyter, IRKernel, and over 80 of the most used R packages and dependencies for data science, and get Conda, the leading package manager for data science. Free Anaconda download.

  Tags: Anaconda, Continuum Analytics, Jupyter, Python, R, R Packages