Top 10 KDnuggets Blog Posts, looking back a year

Have a look at our top blog posts of Q4 2015, some of which continue to be among the most popular on our site, while others are still topical and warrant a second look.

KDnuggets recently reached a milestone of 5,000 posts this past spring. Looking over our content at that time, and subsequently, has brought to light the numerous quality posts we have brought readers, along with the notion that there may be interested in a second look at some of them.

In that vein, the following is a list of our top 10 blog posts of the fourth quarter of 2015. Sure, this is going back nearly a year for these posts, but a few of them are still among our most popular week in, week out, while others are deserving of another glance due to their topical content.

Where to begin?

  1. 7 Steps to Mastering Machine Learning With Python, by Matthew Mayo
    There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!

  2. TensorFlow Disappoints – Google Deep Learning falls shallow, by Matthew Mayo
    Google recently open-sourced its TensorFlow machine learning library, which aims to bring large-scale, distributed machine learning and deep learning to everyone. But does it deliver?

    Note that, while this post has proven popular over the months, it is based on first experiences with TensorFlow, which has evolved since its original release. See the following posts, along with Zachary Chase Lipton's take further below, for a well-rounded, and evolved, view of TensorFlow:

  3. 5 Best Machine Learning APIs for Data Science, by Khushbu Shah
    Machine Learning APIs make it easy for developers to develop predictive applications. Here we review 5 important Machine Learning APIs: IBM Watson, Microsoft Azure Machine Learning, Google Prediction API, Amazon Machine Learning API, and BigML.


  1. TensorFlow is Terrific – A Sober Take on Deep Learning Acceleration, by Zachary Chase Lipton
    TensorFlow does not change the world. But it appears to be the best, most convenient deep learning library out there.

  2. Best Data Science Online Courses, by Brendan Martin
    The number of online data science courses have exploded in recent years and there courses for any needs. Here is a extensive list of free and paid courses from Coursera, DataCamp, Dataquest, edX, Udacity, Udemy, and other major providers.

  3. Top 10 Machine Learning Projects on Github, by Matthew Mayo
    The top 10 machine learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.

ML @ Quora

  1. The Best Advice From Quora on ‘How to Learn Machine Learning’, by Matthew Mayo
    Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information.

  2. Top 5 arXiv Deep Learning Papers, Explained, by Matthew Mayo
    Top deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.

  3. Introduction to Spark with Python, by Srini Kadamati
    Get a handle on using Python with Spark with this hands-on data processing tutorial.

  4. Beginners Guide: Apache Spark Machine Learning with Large Data, by Dmitry Petrov
    This informative tutorial walks us through using Spark's machine learning capabilities and Scala to train a logistic regression classifier on a larger-than-memory dataset.