- 5 Machine Learning Projects You Can No Longer Overlook, April - Apr 13, 2017.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out. Find tools for data exploration, topic modeling, high-level APIs, and feature selection herein.
- Email Spam Filtering: An Implementation with Python and Scikit-learn - Mar 17, 2017.
This post is an overview of a spam filtering implementation using Python and Scikit-learn. The results of 2 classifiers are contrasted and compared: multinomial Naive Bayes and support vector machines.
- K-Means & Other Clustering Algorithms: A Quick Intro with Python - Mar 8, 2017.
In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset.
- A Simple XGBoost Tutorial Using the Iris Dataset - Mar 7, 2017.
This is an overview of the XGBoost machine learning algorithm, which is fast and shows good results. This example uses multiclass prediction with the Iris dataset from Scikit-learn.
- Top /r/MachineLearning Posts, February: Oxford Deep NLP Course; Data Visualization for Scikit-learn Results - Mar 6, 2017.
Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release
- 7 More Steps to Mastering Machine Learning With Python - Mar 1, 2017.
This post is a follow-up to last year's introductory Python machine learning post, which includes a series of tutorials for extending your knowledge beyond the original.
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- 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.
- What is a Support Vector Machine, and Why Would I Use it? - Feb 23, 2017.
Support Vector Machine has become an extremely popular algorithm. In this post I try to give a simple explanation for how it works and give a few examples using the the Python Scikits libraries.
- Learn how to Develop and Deploy a Gradient Boosting Machine Model - Jan 20, 2017.
GBM is one the hottest machine learning methods. Learn how to create GBM using SciKit-Learn and Python and
understand the steps required to transform features, train, and deploy a GBM.
- Top KDnuggets tweets, Jan 04-10: Cartoon: When Self-Driving Car takes you too far; A massive collection of free programming books - Jan 11, 2017.
Also AI #DataScience #MachineLearning: Main Developments 2016, Key Trends 2017; Scikit-Learn Cheat Sheet: #Python #MachineLearning
- 5 Machine Learning Projects You Can No Longer Overlook, January - Jan 2, 2017.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects, the most recent in an ongoing series.
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
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- Top 20 Python Machine Learning Open Source Projects, updated - Nov 21, 2016.
Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.
- Automated Machine Learning: An Interview with Randy Olson, TPOT Lead Developer - Oct 28, 2016.
Read an insightful interview with Randy Olson, Senior Data Scientist at University of Pennsylvania Institute for Biomedical Informatics, and lead developer of TPOT, an open source Python tool that intelligently automates the entire machine learning process.
- KDnuggets™ News 16:n38, Oct 26: Free Machine Learning EBooks; Neural Networks in Python with Scikit-learn - Oct 26, 2016.
5 EBooks to Read Before Getting into A Machine Learning Career; A Beginner's Guide to Neural Networks with Python and Scikit-learn 0.18!; New Poll: What was the largest dataset you analyzed / data mined?; Jupyter Notebook Best Practices for Data Science
- A Beginner’s Guide to Neural Networks with Python and SciKit Learn 0.18! - Oct 20, 2016.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
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- Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team - Oct 4, 2016.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
- O’Reilly Live Training–Real-time. Real experts. Real learning. - Sep 26, 2016.
Get intensive, hands-on training from O'Reilly's expert network on critical data topics - from SQL fundamentals to distributed computing; enterprise strategy to data science at scale.
- Top Machine Learning Projects for Julia - Aug 19, 2016.
Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.
- Contest Winner: Winning the AutoML Challenge with Auto-sklearn - Aug 5, 2016.
This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. It is built around the successful scikit-learn library and won the recent AutoML challenge.
- Would You Survive the Titanic? A Guide to Machine Learning in Python Part 1 - Jul 25, 2016.
Check out the first of a 3 part introductory series on machine learning in Python, fueled by the Titanic dataset. This is a great place to start for a machine learning newcomer.
- Semi-supervised Feature Transfer: The Practical Benefit of Deep Learning Today? - Jul 12, 2016.
This post evaluates four different strategies for solving a problem with machine learning, where customized models built from semi-supervised "deep" features using transfer learning outperform models built from scratch, and rival state-of-the-art methods.
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- 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
- TPOT: A Python Tool for Automating Data Science - May 13, 2016.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
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- Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn - Feb 12, 2016.
Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?
- Auto-Scaling scikit-learn with Spark - Feb 11, 2016.
Databricks gives us an overview of the spark-sklearn library, which automatically and seamlessly distributes model tuning on a Spark cluster, without impacting workflow.
- Scikit-learn and Python Stack Tutorials: Introduction, Implementing Classifiers - Jan 18, 2016.
A small collection of introductory scikit-learn and Python stack tutorials for those with an existing understanding of machine learning looking to jump right into using a new set of tools.
- Top 10 Machine Learning Projects on Github - Dec 14, 2015.
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.
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- Top New Features in Orange 3 Data Mining Platform - Dec 10, 2015.
The main technical advantage of Orange 3 is its integration with NumPy and SciPy libraries. Other improvements include reading online data, working through queries for SQL and pre-processing.
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- Make Beautiful Interactive Data Visualizations Easily, Dec 15 Webinar - Dec 7, 2015.
- 7 Steps to Mastering Machine Learning With Python - Nov 19, 2015.
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!
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- R vs Python: head to head data analysis - Oct 13, 2015.
The epic battle between R vs Python goes on. Here we are comparing both of them in terms of generic tasks of data scientist’s like reading CSV, finding data summary, PCA, model building, plotting, and many more.
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- Top 10 Quora Data Science Writers and Their Best Advice - Sep 17, 2015.
Top Quora data science writers give their advice on pursuing a career in the field, approaching interviews, and selecting appropriate technologies.
- NYC Data Science Academy courses & bootcamps in Data Engineering, Data Science, R, Python, and Machine Learning - Jul 31, 2015.
Upcoming training from NYC Data Science Academy: 6-Week Intensive Data Engineering Bootcamp, 12-Week Data Science Bootcamp, courses in R, Python, Data Science and Machine Learning, and more.
- Continually Updated Data Science IPython Notebooks - Jul 13, 2015.
Continually updated Data Science IPython Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
- Top 20 Python Machine Learning Open Source Projects - Jun 1, 2015.
We examine top Python Machine learning open source projects on Github, both in terms of contributors and commits, and identify most popular and most active ones.
- Top /r/MachineLearning Posts, Apr 5-11: Amazon Machine Learning, Numerical Optimization, and Conditional Random Fields - Apr 14, 2015.
Amazon Machine Learning as a Service, Numerical Optimization, Extracting data from NYTimes recipes, Intro to Machine Learning with sci-kit, and more.
- Top /r/MachineLearning Posts, Mar 29-Apr 4: Andrew Ng AMA, Deep Learning for NLP, and OpenCL Convnets - Apr 10, 2015.
Andrew Ng's upcoming AMA, scikit-learn updates, Richard Socher's Deep Learning NLP videos, Criteo's huge new dataset, and convolutional neural networks on OpenCL are the top topics discussed this week on /r/MachineLearning.
- NYC Data Science Courses, Bootcamps, Meetups - Mar 17, 2015.
NYC Data Science Academy spring schedule includes 3 classes, 3 Meetups, 7 bootcamp events on Data Science, R, Python, Machine Learning, scikit-learn, and related topics.
- Machine Learning Table of Elements Decoded - Mar 11, 2015.
Machine learning packages for Python, Java, Big Data, Lua/JS/Clojure, Scala, C/C++, CV/NLP, and R/Julia are represented using a cute but ill-fitting metaphor of a periodic table. We extract the useful links.
- Top /r/MachineLearning Posts, Mar 1-7: Stanford Deep Learning for NLP, Machine Learning with Scikit-learn - Mar 9, 2015.
This week on /r/MachineLearning, we have a new NLP-focused deep learning course from Stanford, an introduction to scikit-learn, visualization of music collections, an implementation of DeepMind, and NLP using deep learning and Torch.
- Open Source Tools for Machine Learning - Dec 17, 2014.
Open source machine learning software makes it easier to implement machine learning solutions on single computers and at scale, and the diversity of packages provide more options for implementers.
- Top KDnuggets tweets, Dec 8-9: On the effects Analytics bring to enterprises; Use IBM #WatsonAnalytics to Crunch Data For Free - Dec 10, 2014.
On the effects Analytics bring to enterprises; Anyone Can Now Use IBM #WatsonAnalytics to Crunch Data For Free; Economists are NOT nonpartisan - @FiveThirtyEight quantifies their bias; Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning.
- Top KDnuggets tweets, Sep 3-9: What is Big Data – definitions from thought leaders - Sep 12, 2014.
What Is #BigData? Definitions from 40+ thought leaders; Fewer companies are hiring Data Scientists but #DataScience is still hot; Choosing the right estimator scikit-learn #CheatSheet; How do Twitter Analytics show followers gender, when they dont ask?
- Top KDnuggets tweets, Aug 4-5: Ensemble Methods, a brief history; Data Scientist role shifting - Aug 6, 2014.
Ensemble Methods are the backbone of #MachineLearning - a brief history; Data Scientist role shifting, with companies focusing on Developers; To add #MachineLearning for Python, scikit-learn; for Hadoop: Mahout; Meet Fortune 2014 #BigData All-Stars: data scientists, entrepreneurs, CEOs.
- Top KDnuggets tweets, Jun 6-8: Statistical-learning tutorial w. scikit-learn; Data science vs the hunch - Jun 9, 2014.
A tutorial on statistical learning with with scikit-learn ; Data science vs the hunch: When data contradicts manager gut instinct; Stanford University: Data Analyst ; Data Lakes vs Data Warehouses.
- Top KDnuggets tweets, Apr 16-17 - Apr 19, 2014.
Scikit-Learn: a great python library for machine learning; A map of where nobody lives in the US; Apache Spark, the hot new trend in Big Data ; NYU @aghose on Est. Demand for Mobile Apps - Learn more: NYU Stern MS in Biz Analytics.
- Top KDnuggets tweets, Mar 10-11: Deep Learning overview, free book; Best machine learning interview questions - Mar 12, 2014.
Deep Learning: Methods and Application, free book from Microsoft; Best interview questions to evaluate a machine learning researcher; Good list of Machine Learning Libraries in Python: scikit-learn, pandas, Theano, NLTK.