AI News, Category: visualization

Category: visualization

One of the most remarkable features of this year’s Strataconf was the almost universal use of IPython notebooks in presentations and tutorials.

This framework not only allows the speakers to demonstrate each step in the data science approach but also gives the audience an opportunity to do the same –

So, if you want to learn about predictions, modeling and large-scale data analysis, the following resources should give you a fantastic deep dive into these topics: 1) Mining the Social Web by Matthew A.

Russell If you want to learn how to automatically extract information from Twitter streams, Facebook fanpages, Google+ posts, Github accounts and many more information sources, this is the best resource to start.

In this notebook, Olivier explains how to set upand tune machine learning projects such as predictive modeling with the famous Titanic data-set on Kaggle.

The GraphLab library allows very fast access to large data structures with a special data frame format called the SFrame.

This notebook works on the Freebase movie database to find out whether the KevinBacon number really holds true or whether there are other actorsthat are more central in the movie universe.

Peter Norvig is not only the master mind behind the Google economy, teacher of a wonderful introduction to Python programmingat Udacity and author of many scientific papers on applied statistics and modeling, but he also seems to be the true nerd.

Who else would take a xkcd comic strip by the word and work out the regular expression matching patterns that provide a solution to the problem posed in the comic strip.

Comprehensive Beginner’s Guide to Jupyter Notebooks for Data Science & Machine Learning

One of the most common question people ask is which IDE / environment / tool to use, while working on your data science projects.

Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports. They help data scientists streamline their work and enable more productivity and easy collaboration.

By the time you reach the end of the article, you will have a good idea as to why you should leverage it for your machine learning projects and why Jupyter Notebooks are considered better than other standard tools in this domain!

It provides an environment, where you can document your code, run it, look at the outcome, visualize data and see the results without leaving the environment.

This makes it a handy tool for performing end to end data science workflows – data cleaning, statistical modeling, building and training machine learning models, visualizing data, and many, many other uses.

This allows the user to test a specific block of code in a project without having to execute the code from the start of the script.

Anaconda installs both these tools and includes quite a lot of packages commonly used in the data science and machine learning community.

To upgrade to the latest pip version, follow the below code: Once pip is ready, you can go ahead and install Jupyter: You can view the official Jupyter installation documentation here.

Once you do this, the Jupyter notebook will open up in your default web browser with the below URL: http://localhost:8888/tree In some cases, it might not open up automatically.

In the menu just above the code, you have options to play around with the cells: add, edit, cut, move cells up and down, run the code in the cell, stop the code, save your work and restart the kernel.

The developers have inserted pre-defined magic functions that make your life easier and your work far more interactive.

You can run the below command to see a list of these functions (note: the “%” is not needed usually because Automagic is usually turned on): You’ll see a lot of options listed and you might even recognise a few!

Now, magic commands run in two ways: As the name suggests, line-wise is when you want to execute a single command line while cell-wise is when you want to execute not just a line, but the entire block of code in the entire cell.

Check out this comprehensive article which is focused on learning data science for a Julia user and includes a section on how to leverage it within the Jupyter environment.

Before you go about adding widgets, you need to import the widgets package: The basic type of widgets are your typical text input, input-based, and buttons.

Command mode binds the keyboard to notebook level commands and is indicated by a grey cell border with a blue left margin.

Edit mode allows you to type text (or code) into the active cell and is indicated by a green cell border.

Once you are in command mode (that is, you don’t have an active cell), you can try out the below shortcuts: When in edit mode (press Enter when in command mode to get into Edit mode), you will find the below shortcuts handy: To see the entire list of keyboard shortcuts, press ‘H’

It takes two simple steps to install it on your machine (there are other methods as well but I found this the most convenient): Step 1: Install it from pip: Step 2: Install the associated JavaScript and CSS files: Once you’re done with this, you’ll see a ‘Nbextensions’

The most commonly used is either a .ipynb file so the other person can replicate your code on their machine or the .html one which opens as a web page (this comes in handy when you want to save the images embedded in the Notebook).

You can also edit popular file formats like Markdown, CSV and JSON with a live preview to see the changes happening in real time in the actual file.

While working alone on projects can be fun, most of the time you’ll find yourself working within a team.

And in that situation, it’s very important to follow guidelines and best practices to ensure your code and Jupyter Notebooks are annotated properly so as to be consistent with your team members.

A gallery of interesting Jupyter Notebooks

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file.

These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages): The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language.

The interactive plotting library Nyaplot has some case studies using IRuby: This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication.


Anaconda is a free distribution of the Python programming language for large-scale data processing, predictive analytics, and scientific computing that aims to simplify package management and deployment.

For detailed instructions, scripts, and tools to set up your development environment for data analysis, check out the dev-setup repo.

To view interactive content or to modify elements within the IPython notebooks, you must first clone or download the repository then run the notebook.

am providing code and resources in this repository to you under an open source license.

Because this is my personal repository, the license you receive to my code and resources is from me and not my employer (Facebook).

Introducing Oracle Machine Learning SQL Notebooks for the Oracle Autonomous Data Warehouse Cloud!

Oracle Machine Learningis a new SQL notebook interface for data scientists to perform machine learning in the Oracle Autonomous Data Warehouse Cloud (ADWC).

Notebook technologies support the creation of scripts while supporting the documentation of assumptions, approaches and rationale to increase data science team productivity.Oracle Machine Learning SQL notebooks, based on Apache Zeppelin technology, enable teams to collaborate to build, evaluate and deploy predictive models and analytical methodologies in the Oracle ADWC.

Oracle Machine Learning SQL notebooks provide easy access to Oracle's parallelized, scalable in-database implementations of a library of Oracle Advanced Analytics'machine learning algorithms (classification, regression, anomaly detection,clustering, associations, attribute importance,feature extraction, times series, etc.), SQL, PL/SQL and Oracle's statistical and analytical SQL functions.

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