scikit-learn (87)

• The 20 Python Packages You Need For Machine Learning and Data Science - Oct 14, 2021.
  
  Do you do Python? Do you do data science and machine learning? Then, you need to do these crucial Python libraries that enable nearly all you will want to do.

  Data Science, Keras, Machine Learning, Matplotlib, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, TensorFlow

• AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch - Oct 11, 2021.
  
  AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.

  Automated Machine Learning, AutoML, Python, PyTorch, scikit-learn

• 30 Most Asked Machine Learning Questions Answered - Aug 3, 2021.
  
  There is always a lot to learn in machine learning. Whether you are new to the field or a seasoned practitioner and ready for a refresher, understanding these key concepts will keep your skills honed in the right direction.

  Beginners, Interview Questions, Machine Learning, Regression, scikit-learn

• A Comprehensive Guide to Ensemble Learning – Exactly What You Need to Know - May 6, 2021.
  
  This article covers ensemble learning methods, and exactly what you need to know in order to understand and implement them.

  CatBoost, Ensemble Methods, Machine Learning, Python, random forests algorithm, scikit-learn, XGBoost

• Gradient Boosted Decision Trees – A Conceptual Explanation - Apr 30, 2021.
  
  Gradient boosted decision trees involves implementing several models and aggregating their results. These boosted models have become popular thanks to their performance in machine learning competitions on Kaggle. In this article, we’ll see what gradient boosted decision trees are all about.

  CatBoost, Decision Trees, Gradient Boosting, Machine Learning, Python, scikit-learn, XGBoost

• The Most In-Demand Skills for Data Scientists in 2021 - Apr 15, 2021.
  
  If you are preparing to make a career as a Data Scientist or are looking for opportunities to skill-up in your current role, this analysis of in-demand skills for 2021, based on over 15,000 Data Scientist job postings, should offer you a good idea as to which programming languages and software tools are increasing and decreasing in importance.

  AWS, Data Science Skills, Python, PyTorch, R, scikit-learn, SQL, TensorFlow

• Top 10 Python Libraries Data Scientists should know in 2021 - Mar 24, 2021.
  
  So many Python libraries exist that offer powerful and efficient foundations for supporting your data science work and machine learning model development. While the list may seem overwhelming, there are certain libraries you should focus your time on, as they are some of the most commonly used today.

  Data Science, Keras, numpy, Pandas, Python, scikit-learn, Seaborn, TensorFlow

• The Best Machine Learning Frameworks & Extensions for Scikit-learn - Mar 22, 2021.
  
  Learn how to use a selection of packages to extend the functionality of Scikit-learn estimators.

  Machine Learning, Python, scikit-learn

• Speeding up Scikit-Learn Model Training - Mar 5, 2021.
  
  If your scikit-learn models are taking a bit of time to train, then there are several techniques you can use to make the processing more efficient. From optimizing your model configuration to leveraging libraries to speed up training through parallelization, you can build the best scikit-learn model possible in the least amount of time.

  Distributed Computing, Machine Learning, Optimization, scikit-learn

• Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - Mar 5, 2021.
  
  PyCaret, a low code Python ML library, offers several ways to tune the hyper-parameters of a created model. In this post, I'd like to show how Ray Tune is integrated with PyCaret, and how easy it is to leverage its algorithms and distributed computing to achieve results superior to default random search method.

  Bayesian, Hyperparameter, Machine Learning, Optimization, PyCaret, Python, scikit-learn

• Distributed and Scalable Machine Learning [Webinar] - Feb 17, 2021.
  
  Mike McCarty and Gil Forsyth work at the Capital One Center for Machine Learning, where they are building internal PyData libraries that scale with Dask and RAPIDS. For this webinar, Feb 23 @ 2 pm PST, 5pm EST, they’ll join Hugo Bowne-Anderson and Matthew Rocklin to discuss their journey to scale data science and machine learning in Python.

  Capital One, Dask, Distributed, Machine Learning, Python, scikit-learn, XGBoost

• How to Speed up Scikit-Learn Model Training - Feb 11, 2021.
  
  Scikit-Learn is an easy to use a Python library for machine learning. However, sometimes scikit-learn models can take a long time to train. The question becomes, how do you create the best scikit-learn model in the least amount of time?

  Distributed Systems, Hyperparameter, Machine Learning, Optimization, Parallelism, Python, scikit-learn, Training

• Build Your First Data Science Application - Feb 4, 2021.
  
  Check out these seven Python libraries to make your first data science MVP application.

  API, Data Science, Jupyter, Keras, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn

• The Ultimate Scikit-Learn Machine Learning Cheatsheet - Jan 25, 2021.
  
  With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data transformation.

  Cheat Sheet, Machine Learning, scikit-learn

• K-Means 8x faster, 27x lower error than Scikit-learn in 25 lines - Jan 15, 2021.
  
  K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.

  Algorithms, K-means, Machine Learning, scikit-learn

• How to use Machine Learning for Anomaly Detection and Conditional Monitoring - Dec 16, 2020.
  
  This article explains the goals of anomaly detection and outlines the approaches used to solve specific use cases for anomaly detection and condition monitoring.

  Anomaly Detection, Machine Learning, Python, scikit-learn, Unsupervised Learning

• 5 Most Useful Machine Learning Tools every lazy full-stack data scientist should use - Nov 18, 2020.
  
  If you consider yourself a Data Scientist who can take any project from data curation to solution deployment, then you know there are many tools available today to help you get the job done. The trouble is that there are too many choices. Here is a review of five sets of tools that should turn you into the most efficient full-stack data scientist possible.

  Data Science Tools, Data Scientist, GitHub, Heroku, Machine Learning, Postgres, PyCharm, PyTorch, scikit-learn, Streamlit

• Most Popular Distance Metrics Used in KNN and When to Use Them - Nov 11, 2020.
  
  For calculating distances KNN uses a distance metric from the list of available metrics. Read this article for an overview of these metrics, and when they should be considered for use.

  K-nearest neighbors, Metrics, scikit-learn

• Feature Ranking with Recursive Feature Elimination in Scikit-Learn - Oct 19, 2020.
  
  This article covers using scikit-learn to obtain the optimal number of features for your machine learning project.

  Feature Selection, Machine Learning, Python, scikit-learn

• Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills - Sep 8, 2020.
  
  We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.

  Communication, Data Preparation, Data Science Skills, Data Visualization, Excel, GitHub, Mathematics, Poll, Python, Reinforcement Learning, scikit-learn, SQL, Statistics

• 10 Things You Didn’t Know About Scikit-Learn - Sep 3, 2020.
  
  Check out these 10 things you didn’t know about Scikit-Learn... until now.

  Machine Learning, Python, scikit-learn

• Why would you put Scikit-learn in the browser? - Jul 22, 2020.
  
  Honestly? I don’t know. But I do think WebAssembly is a good target for ML/AI deployment (in the browser and beyond).

  Deployment, Development, scikit-learn, Virtualization

• Simplified Mixed Feature Type Preprocessing in Scikit-Learn with Pipelines - Jun 16, 2020.
  
  There is a quick and easy way to perform preprocessing on mixed feature type data in Scikit-Learn, which can be integrated into your machine learning pipelines.

  Data Preprocessing, Pipeline, Python, scikit-learn

• Dataset Splitting Best Practices in Python - May 26, 2020.
  
  If you are splitting your dataset into training and testing data you need to keep some things in mind. This discussion of 3 best practices to keep in mind when doing so includes demonstration of how to implement these particular considerations in Python.

  Datasets, Python, scikit-learn, Training Data, Validation

• Introduction to the K-nearest Neighbour Algorithm Using Examples - Apr 1, 2020.
  
  Read this concise summary of KNN, a supervised and pattern classification learning algorithm which helps us find which class the new input belongs to when k nearest neighbours are chosen and distance is calculated between them.

  Algorithms, K-nearest neighbors, Machine Learning, Python, scikit-learn

• Practical Hyperparameter Optimization - Feb 13, 2020.
  
  An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.

  Automated Machine Learning, AutoML, Deep Learning, Hyperparameter, Machine Learning, Optimization, Python, scikit-learn

• 5 Great New Features in Latest Scikit-learn Release - Dec 10, 2019.
  
  From not sweating missing values, to determining feature importance for any estimator, to support for stacking, and a new plotting API, here are 5 new features of the latest release of Scikit-learn which deserve your attention.

  Data Preparation, Data Preprocessing, Ensemble Methods, Feature Selection, Gradient Boosting, K-nearest neighbors, Machine Learning, Missing Values, Python, scikit-learn, Visualization

• Beginners Guide to the Three Types of Machine Learning - Nov 13, 2019.
  
  The following article is an introduction to classification and regression — which are known as supervised learning — and unsupervised learning — which in the context of machine learning applications often refers to clustering — and will include a walkthrough in the popular python library scikit-learn.

  Beginners, Classification, Machine Learning, Python, Regression, scikit-learn, Supervised Learning, Unsupervised Learning

• How to Extend Scikit-learn and Bring Sanity to Your Machine Learning Workflow - Oct 29, 2019.
  
  In this post, learn how to extend Scikit-learn code to make your experiments easier to maintain and reproduce.

  Machine Learning, Python, scikit-learn, Software Engineering, Workflow

• Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning - Sep 19, 2019.
  
  While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.

  Dataset, Machine Learning, scikit-learn, Synthetic Data

• Train sklearn 100x Faster - Sep 11, 2019.
  
  As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

  Distributed Systems, Machine Learning, Python, scikit-learn, Training

• Scikit-Learn vs mlr for Machine Learning - Sep 10, 2019.
  
  How does the scikit-learn machine learning library for Python compare to the mlr package for R? Following along with a machine learning workflow through each approach, and see if you can gain a competitive advantage by knowing both frameworks.

  Exxact, Machine Learning, R, scikit-learn

• Understanding Decision Trees for Classification in Python - Aug 21, 2019.
  
  This tutorial covers decision trees for classification also known as classification trees, including the anatomy of classification trees, how classification trees make predictions, using scikit-learn to make classification trees, and hyperparameter tuning.

  Classification, Decision Trees, Python, scikit-learn

• How to Learn Python for Data Science the Right Way - Jun 14, 2019.
  
  The biggest mistake you can make while learning Python for data science is to learn Python programming from courses meant for programmers. Avoid this mistake, and learn Python the right way by following this approach.

  Advice, Data Science, Jupyter, Matplotlib, Pandas, Python, scikit-learn, StatsModels

• What you need to know: The Modern Open-Source Data Science/Machine Learning Ecosystem - Jun 10, 2019.
  
  We identify the 6 tools in the modern open-source Data Science ecosystem, examine the Python vs R question, and determine which tools are used the most with Deep Learning and Big Data.

  Anaconda, Apache Spark, Big Data Software, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, Tableau, TensorFlow

• 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
  
  This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!

  7 Steps, Classification, Cross-validation, Dimensionality Reduction, Feature Engineering, Feature Selection, Image Classification, K-nearest neighbors, Machine Learning, Modeling, Naive Bayes, numpy, Pandas, PCA, Python, scikit-learn, Transfer Learning

• Python leads the 11 top Data Science, Machine Learning platforms: Trends and Analysis - May 30, 2019.
  
  Python continues to lead the top Data Science platforms, but R and RapidMiner hold their share; Almost 50% have used Deep Learning tools; SQL is steady; Consolidation continues.

  Pages: 1 2

  Anaconda, Apache Spark, Deep Learning, Excel, Keras, Poll, Python, R, RapidMiner, scikit-learn, Software, SQL, TensorFlow

• Naive Bayes: A Baseline Model for Machine Learning Classification Performance - May 7, 2019.
  
  We can use Pandas to conduct Bayes Theorem and Scikitlearn to implement the Naive Bayes Algorithm. We take a step by step approach to understand Bayes and implementing the different options in Scikitlearn.

  Pages: 1 2

  Algorithms, Data Science, Machine Learning, Naive Bayes, Python, scikit-learn, Statistics

• A Beginner’s Guide to Linear Regression in Python with Scikit-Learn - Mar 29, 2019.
  
  What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.

  Pages: 1 2

  Beginners, Linear Regression, Python, scikit-learn

• Feature Reduction using Genetic Algorithm with Python - Mar 25, 2019.
  
  This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.

  Pages: 1 2

  Deep Learning, Feature Engineering, Genetic Algorithm, Neural Networks, numpy, Python, scikit-learn

• Python Data Science for Beginners - Feb 20, 2019.
  
  Python’s syntax is very clean and short in length. Python is open-source and a portable language which supports a large standard library. Buy why Python for data science? Read on to find out more.

  Beginners, Data Science, Matplotlib, numpy, Pandas, Python, scikit-learn, SciPy

• An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning - Feb 13, 2019.
  
  If you’re going to do Machine Learning in Python, Scikit Learn is the gold standard. Scikit-learn provides a wide selection of supervised and unsupervised learning algorithms. Best of all, it’s by far the easiest and cleanest ML library.

  Machine Learning, Python, scikit-learn

• Automated Machine Learning in Python - Jan 18, 2019.
  
  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn

• A Guide to Decision Trees for Machine Learning and Data Science - Dec 24, 2018.
  
  What makes decision trees special in the realm of ML models is really their clarity of information representation. The “knowledge” learned by a decision tree through training is directly formulated into a hierarchical structure.

  Algorithms, Data Science, Decision Trees, Machine Learning, Python, scikit-learn

• Notes on Feature Preprocessing: The What, the Why, and the How - Oct 26, 2018.
  
  This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.

  Data Preparation, Data Preprocessing, numpy, Python, scikit-learn, SciPy

• Iterative Initial Centroid Search via Sampling for k-Means Clustering - Sep 12, 2018.
  
  Thinking about ways to find a better set of initial centroid positions is a valid approach to optimizing the k-means clustering process. This post outlines just such an approach.

  Clustering, K-means, Python, Sampling, scikit-learn

• Multi-Class Text Classification with Scikit-Learn - Aug 27, 2018.
  
  The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.

  NLP, Python, scikit-learn, Text Classification, Text Mining

• Building Reliable Machine Learning Models with Cross-validation - Aug 9, 2018.
  
  Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.

  Comet.ml, Cross-validation, Machine Learning, Modeling, scikit-learn

• Top 20 Python Libraries for Data Science in 2018 - Jun 27, 2018.
  
  Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.

  Pages: 1 2

  Bokeh, Data Science, Keras, Matplotlib, NLTK, numpy, Pandas, Plotly, Python, PyTorch, scikit-learn, SciPy, Seaborn, TensorFlow, XGBoost

• The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R? - Jun 6, 2018.
  
  We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.

  Anaconda, Apache Spark, Data Science, Keras, Machine Learning, Open Source, Poll, Python, R, RapidMiner, Scala, scikit-learn, TensorFlow

• Top 20 Python AI and Machine Learning Open Source Projects - Feb 20, 2018.
  
  We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.

  GitHub, Machine Learning, Open Source, Python, scikit-learn, TensorFlow

• 5 Machine Learning Projects You Should Not Overlook - Feb 8, 2018.
  
  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!

  Bayesian, Gradient Boosting, Keras, Machine Learning, Overlook, PHP, Python, scikit-learn

• Using AutoML to Generate Machine Learning Pipelines with TPOT - Jan 29, 2018.
  
  This post will take a different approach to constructing pipelines. Certainly the title gives away this difference: instead of hand-crafting pipelines and hyperparameter optimization, and performing model selection ourselves, we will instead automate these processes.

  Automated Machine Learning, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow

• Managing Machine Learning Workflows with Scikit-learn Pipelines Part 3: Multiple Models, Pipelines, and Grid Searches - Jan 24, 2018.
  
  In this post, we will be using grid search to optimize models built from a number of different types estimators, which we will then compare and properly evaluate the best hyperparameters that each model has to offer.

  Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow

• Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search - Jan 19, 2018.
  
  Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.

  Data Preprocessing, Hyperparameter, Optimization, Pipeline, Python, scikit-learn, Workflow

• Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction - Dec 7, 2017.
  
  Scikit-learn's Pipeline class is designed as a manageable way to apply a series of data transformations followed by the application of an estimator.

  Data Preprocessing, Pipeline, Python, scikit-learn, Workflow

• Visualizing Cross-validation Code - Sep 5, 2017.
  
  Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.

  Cross-validation, Machine Learning, Python, scikit-learn

• Introducing Dask-SearchCV: Distributed hyperparameter optimization with Scikit-Learn - May 12, 2017.
  
  We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.

  Dask, Distributed Computing, Distributed Systems, Machine Learning, Optimization, scikit-learn

• The Guerrilla Guide to Machine Learning with Python - May 1, 2017.
  
  Here is a bare bones take on learning machine learning with Python, a complete course for the quick study hacker with no time (or patience) to spare.

  Deep Learning, Machine Learning, Pandas, Python, scikit-learn, Sebastian Raschka

• 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.

  Data Exploration, Deep Learning, Java, Machine Learning, Neural Networks, Overlook, Python, Scala, scikit-learn, Topic Modeling

• 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.

  Machine Learning, Python, scikit-learn

• 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.

  Clustering, K-means, Python, scikit-learn

• 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.

  Python, scikit-learn, XGBoost

• 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.
   
   

  Pages: 1 2

  7 Steps, Classification, Clustering, Deep Learning, Ensemble Methods, Gradient Boosting, Machine Learning, Python, scikit-learn, Sebastian Raschka

• 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.

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

• 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.

  Python, scikit-learn, Support Vector Machines, SVM, Yhat

• 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.

  Gradient Boosting, Open Data Group, Python, scikit-learn

• 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

  2017 Predictions, Free ebook, Programming, scikit-learn, Self-Driving Car

• 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.
   
   

  Boosting, C++, Data Preparation, Decision Trees, Machine Learning, Neural Networks, Optimization, Overlook, Pandas, Python, scikit-learn

• 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.

  Pages: 1 2

  Beginners, Classification, Clustering, Machine Learning, Pandas, Python, R, scikit-learn, Software Developer

• 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.

  GitHub, Machine Learning, Open Source, Python, scikit-learn

• 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.

  Automated Data Science, Automated Machine Learning, Machine Learning, Python, scikit-learn

• 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.

  Pages: 1 2

  Beginners, Machine Learning, Neural Networks, Python, scikit-learn

• 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.

  Automated, Automated Data Science, Automated Machine Learning, Competition, Machine Learning, scikit-learn

• 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.

  Deep Learning, Julia, Machine Learning, Open Source, scikit-learn

• 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.

  Automated, Automated Data Science, Automated Machine Learning, Competition, Hyperparameter, scikit-learn, Weka

• 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.

  Machine Learning, Python, scikit-learn, Titanic

• 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.

  Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano

• 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?

  Deep Learning, Google, Matthew Mayo, Python, scikit-learn, TensorFlow

• 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.

  Apache Spark, Databricks, Open Source, scikit-learn

• 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.

  Pages: 1 2

  GitHub, Machine Learning, Matthew Mayo, Open Source, scikit-learn, Top 10

• 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.

  Pages: 1 2

  Data Mining, Data Visualization, numpy, Orange, Python, scikit-learn

• Make Beautiful Interactive Data Visualizations Easily, Dec 15 Webinar - Dec 7, 2015.
  
  Learn how to use Bokeh interactive visualization framework for open data science to create rich, interactive visualizations in the browser, without writing a line of JavaScript, HTML, or CSS.

  Anaconda, Bokeh, Continuum Analytics, Data Visualization, scikit-learn

• 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!

  Pages: 1 2

  7 Steps, Anaconda, Caffe, Deep Learning, Machine Learning, Matthew Mayo, Python, scikit-learn, Theano

• 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.

  Data Science, Quora, scikit-learn, Top 10

• 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.

  GitHub, Machine Learning, Open Source, Python, scikit-learn

• 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.

  Big Data Software, Java, Julia, Machine Learning, NLP, Python, R, Scala, scikit-learn, Weka