Machine Learning (1326)

• How Much Mathematics Does an IT Engineer Need to Learn to Get Into Data Science? - Dec 20, 2017.
  
  When I started diving deep into these exciting subjects (by self-study), I discovered quickly that I don’t know/only have a rudimentary idea about/ forgot mostly what I studied in my undergraduate study some essential mathematics.

  Data Science, Engineer, Machine Learning, Mathematics

• Getting Started with TensorFlow: A Machine Learning Tutorial - Dec 19, 2017.
  
  A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.

  Pages: 1 2

  Machine Learning, Python, TensorFlow

• NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
  
  Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.

  Bias, Conference, Machine Learning, NeurIPS, NIPS, Reinforcement Learning

• Machine Learning & Artificial Intelligence: Main Developments in 2017 and Key Trends in 2018 - Dec 15, 2017.
  
  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2017 and their 2018 key trend predictions.

  2018 Predictions, AI, Ajit Jaokar, Brandon Rohrer, Daniel Tunkelang, Hugo Larochelle, Machine Learning, Pedro Domingos, Sebastian Raschka, Xavier Amatriain

• How to Improve Machine Learning Performance? Lessons from Andrew Ng - Dec 13, 2017.
  
  5 useful tips and lessons from Andrew Ng on how to improve your Machine Learning performance, including Orthogonalisation, Single Number Evaluation Metric, and Satisfying and Optimizing Metric.

  Andrew Ng, Machine Learning

• Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018 - Dec 12, 2017.
  
  The leading experts in the field on the main Data Science, Machine Learning, Predictive Analytics developments in 2017 and key trends in 2018.

  2018 Predictions, Carla Gentry, Data Science, Eric Siegel, Jeff Ullman, Jen Underwood, Jill Dyche, Kirk D. Borne, Machine Learning, Predictions, Rexer Analytics, Tom Davenport, Trends

• Exclusive: Interview with Rich Sutton, the Father of Reinforcement Learning - Dec 5, 2017.
  
  My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.

  AGI, AI, AlphaGo, DeepMind, Machine Learning, Neuroscience, Reinforcement Learning, Richard Sutton, Yann LeCun

• Natural Language Processing Library for Apache Spark – free to use - Nov 28, 2017.
  
  Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.

  Apache Spark, API, GitHub, John Snow Labs, Machine Learning, NLP

• How To Unit Test Machine Learning Code - Nov 28, 2017.
  
  One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.

  Machine Learning, Neural Networks, Python, Software Engineering, TensorFlow

• New Poll: Which Data Science / Machine Learning methods and tools you used? - Nov 20, 2017.
  
  Please vote in new KDnuggets poll which examines the methods and tools used for a real-world application or project.

  Algorithms, Data Science Tools, Machine Learning, Poll

• You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
  
  Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.

  Data Science, Linear Regression, Machine Learning, Multicollinearity, Statistics

• The 10 Statistical Techniques Data Scientists Need to Master - Nov 15, 2017.
  
  The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.

  Pages: 1 2

  Algorithms, Data Science, Data Scientist, Machine Learning, Statistical Learning, Statistics

• Machine Learning Algorithms: Which One to Choose for Your Problem - Nov 14, 2017.
  
  This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.

  Algorithms, Machine Learning, Reinforcement Learning, Statsbot, Supervised Learning, Unsupervised Learning

• When Will Demand for Data Scientists/Machine Learning Experts Peak? - Nov 7, 2017.
  
  We analyze the results of Data Science / Machine Learning peak demand poll, examine the split between optimists and pessimists, and try to explain why predictions look so similar regardless of experience, affiliation, and region?

  Data Scientist, Hiring, Machine Learning, Poll, Trends

• Interpreting Machine Learning Models: An Overview - Nov 7, 2017.
  
  This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.

  Interpretability, Machine Learning, Modeling, O'Reilly

• What is the difference between Bagging and Boosting? - Nov 6, 2017.
  
  Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Here we explain in detail.

  Bagging, Boosting, Ensemble Methods, Machine Learning

• 3 different types of machine learning - Nov 1, 2017.
  
  In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.

  Pages: 1 2

  Classification, Clustering, Machine Learning, Regression, Reinforcement Learning, Supervised Learning

• Getting Started with Machine Learning in One Hour! - Nov 1, 2017.
  
  Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.

  Beginners, Machine Learning

• 6 Books Every Data Scientist Should Keep Nearby - Oct 31, 2017.
  
  The best way to stay in touch is to continue brushing up on your knowledge while also maintaining experience. It’s the perfect storm or combination of skills to help you succeed in the industry.

  Books, Data Science, Data Scientist, Machine Learning

• Top 6 errors novice machine learning engineers make - Oct 30, 2017.
  
  What common mistakes beginners do when working on machine learning or data science projects? Here we present list of such most common errors.

  Beginners, Machine Learning, Mistakes, Outliers, Regression, Regularization, Time Series

• 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
  
  Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.

  7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks

• XGBoost: A Concise Technical Overview - Oct 27, 2017.
  
  Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.

  Explained, Kaggle, Machine Learning, XGBoost

• Density Based Spatial Clustering of Applications with Noise (DBSCAN) - Oct 26, 2017.
  
  DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.

  Algorithms, Clustering, DBSCAN, Machine Learning

• Top 10 Machine Learning with R Videos - Oct 24, 2017.
  
  A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.

  Algorithms, Clustering, K-nearest neighbors, Machine Learning, PCA, R, Text Mining, Top 10, Youtube

• Rethinking 3 Laws of Machine Learning - Oct 23, 2017.
  
  We rethink Asimov’s 3 law of robotics to help companies moving to unsupervised machine learning and realize 100% automated predictive information governance (PIG).

  AI, Humor, Machine Learning, Robots, Skynet

• Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
  
  A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
   
   

  Pages: 1 2

  Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10

• Random Forests®, Explained - Oct 17, 2017.
  
  Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.

  Algorithms, CART, Decision Trees, Ensemble Methods, Explained, Machine Learning, random forests algorithm, Salford Systems

• How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool - Oct 16, 2017.
  
  In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.

  Deepak Agarwal, LinkedIn, Machine Learning, Recommendation, Recommender Systems

• Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018 - Oct 12, 2017.
  
  Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.

  Bootcamp, Data Science, Data Visualization, Machine Learning, NLP, Python, R, Switzerland, Zurich

• Learn Generalized Linear Models (GLM) using R - Oct 11, 2017.
  
  In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.

  Pages: 1 2

  Generalized Linear Models, Linear Regression, Logistic Regression, Machine Learning, R, Regression

• A Quick Guide to Fake News Detection on Social Media - Oct 10, 2017.
  
  Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.

  Fake News, Machine Learning, Social Media

• Using Machine Learning to Predict and Explain Employee Attrition - Oct 4, 2017.
  
  Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.

  Churn, H2O, HR, Machine Learning, Predictive Analytics, Workforce Analytics

• XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
  
  Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.

  Algorithms, Data Science, Explained, Kaggle, Machine Learning

• Understanding Machine Learning Algorithms - Oct 3, 2017.
  
  Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.

  Algorithms, Ensemble Methods, Gradient Boosting, Machine Learning, Neural Networks, Predictive Analytics, random forests algorithm, SVM

• Top 10 Videos on Machine Learning in Finance - Sep 29, 2017.
  
  Talks, tutorials and playlists – you could not get a more gentle introduction to Machine Learning (ML) in Finance. Got a quick 4 minutes or ready to study for hours on end? These videos cover all skill levels and time constraints!

  Credit Risk, Finance, Investment Portfolio, Machine Learning, Python, R, Stocks, Tutorials, Videolectures, Youtube

• Top 10 Active Big Data, Data Science, Machine Learning Influencers on LinkedIn, Updated - Sep 26, 2017.
  
  Looking for advice? Guidance? Stories? We’ve put a list of the top ten LinkedIn influencers of the last three months, follow them and stay up-to-date with the latest news in Big Data, Data Science, Analytics, Machine Learning and AI.

  About Gregory Piatetsky, Bernard Marr, Big Data, Carla Gentry, Data Science, DJ Patil, Influencers, Kirk D. Borne, LinkedIn, Machine Learning, Tom Davenport, Trends

• Visualizing High Dimensional Data In Augmented Reality - Sep 25, 2017.
  
  When Data Scientists first get a data set, they oftne use a matrix of 2D scatter plots to quickly see the contents and relationships between pairs of attributes. But for data with lots of attributes, such analysis does not scale.

  Data Science, Data Visualization, IBM, Instacart, Machine Learning, R

• Ensemble Learning to Improve Machine Learning Results - Sep 22, 2017.
  
  Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking).

  Pages: 1 2

  Ensemble Methods, Machine Learning, Statsbot

• 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets - Sep 22, 2017.
  
  This collection of data science cheat sheets is not a cheat sheet dump, but a curated list of reference materials spanning a number of disciplines and tools.

  Pages: 1 2 3

  Cheat Sheet, Data Science, Deep Learning, Machine Learning, Neural Networks, Probability, Python, R, SQL, Statistics

• 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
  
  We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.

  Deep Learning, Machine Learning, Neural Networks, Reinforcement Learning

• Machine Learning Translation and the Google Translate Algorithm - Sep 14, 2017.
  
  Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.

  Google, Machine Learning, Machine Translation, Natural Language Processing, Statsbot

• New-Age Machine Learning Algorithms in Retail Lending - Sep 13, 2017.
  
  We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.

  Credit Risk, Customer Analytics, Deep Learning, Fintech, Machine Learning, Recurrent Neural Networks

• K-Nearest Neighbors – the Laziest Machine Learning Technique - Sep 12, 2017.
  
  K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.

  Algorithms, K-nearest neighbors, Machine Learning, RapidMiner

• Python vs R – Who Is Really Ahead in Data Science, Machine Learning? - Sep 12, 2017.
  
  We examine Google Trends, job trends, and more and note that while Python has only a small advantage among current Data Science and Machine Learning related jobs, this advantage is likely to increase in the future.

  Data Science, Google Trends, Jobs, Kaggle, Machine Learning, Python, Python vs R, R

• Top 10 Machine Learning Use Cases: Part 2 - Sep 11, 2017.
  
  This post is the second in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.

  Healthcare, IBM, Machine Learning, Use Cases

• Putting the “Science” Back in Data Science - Sep 6, 2017.
  
  The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.

  Business, Data Science, Machine Learning, Process, Rubens Zimbres

• 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

• Cartoon: Future Machine Learning Class - Sep 2, 2017.
  
  New KDnuggets Cartoon looks at an unusual but possible future Machine Learning Class.

  Cartoon, Data Cleaning, Machine Learning

• Top 10 Machine Learning Use Cases: Part 1 - Aug 31, 2017.
  
  This post is the first in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.

  Belgium, Colombia, Government, IBM, IBM SPSS Modeler, Machine Learning, Use Cases

• Learning Machine Learning… with Flashcards - Aug 31, 2017.
  
  Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.

  Education, Machine Learning

• Are physicians worried about computers machine learning their jobs? - Aug 30, 2017.
  
  We review JAMA article on “Unintended Consequences of Machine Learning in Medicine” and argue that a number of alarming opinions in this pieces are not supported by evidence.

  Automation, Decision Support, Healthcare, Machine Learning

• Vital Statistics You Never Learned… Because They’re Never Taught - Aug 29, 2017.
  
  Marketing scientist Kevin Gray asks Professor Frank Harrell about some important things we often get wrong about statistics.

  Bayesian, Data Science, Machine Learning, Statistics

• Support Vector Machine (SVM) Tutorial: Learning SVMs From Examples - Aug 28, 2017.
  
  In this post, we will try to gain a high-level understanding of how SVMs work. I’ll focus on developing intuition rather than rigor. What that essentially means is we will skip as much of the math as possible and develop a strong intuition of the working principle.

  Pages: 1 2 3

  Algorithms, Machine Learning, Statsbot, Support Vector Machines, SVM

• 42 Steps to Mastering Data Science - Aug 25, 2017.
  
  This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.

  Data Preparation, Data Science, Deep Learning, Machine Learning, NoSQL, Python, SQL

• Understanding overfitting: an inaccurate meme in Machine Learning - Aug 23, 2017.
  
  Applying cross-validation prevents overfitting is a popular meme, but is not actually true – it more of an urban legend. We examine what is true and how overfitting is different from overtraining.

  Cross-validation, Machine Learning, Overfitting

• Machine Learning vs. Statistics: The Texas Death Match of Data Science - Aug 23, 2017.
  
  Throughout its history, Machine Learning (ML) has coexisted with Statistics uneasily, like an ex-boyfriend accidentally seated with the groom’s family at a wedding reception: both uncertain where to lead the conversation, but painfully aware of the potential for awkwardness.

  Machine Learning, Statistics

• What is the most important step in a machine learning project? - Aug 18, 2017.
  
  In any machine learning project, business understanding is very important. But in practice, it does not get enough attention. Here we explain what questions should be asked.

  Business, CRISP-DM, Machine Learning, Methodology

• Lessons Learned From Benchmarking Fast Machine Learning Algorithms - Aug 16, 2017.
  
  Boosted decision trees are responsible for more than half of the winning solutions in machine learning challenges hosted at Kaggle, and require minimal tuning. We evaluate two popular tree boosting software packages: XGBoost and LightGBM and draw 4 important lessons.

  Benchmark, Decision Trees, Kaggle, Machine Learning, Microsoft, XGBoost

• 4 Industries Being Transformed by Machine Learning and Robotics - Aug 15, 2017.
  
  When used in combination with big data and machine learning, both AI and robotics can actively improve over time as they collect more information. You don’t have to look far to see how these technologies have revolutionized the world, and continue to do so.

  AI, Automation, Industry, Machine Learning, Robots

• What Artificial Intelligence and Machine Learning Can Do—And What It Can’t - Aug 10, 2017.
  
  I have seen situations where AI (or at least machine learning) had an incredible impact on a business—I also have seen situations where this was not the case. So, what was the difference?

  AI, Andrew Ng, Ingo Mierswa, Machine Learning, RapidMiner

• Going deeper with recurrent networks: Sequence to Bag of Words Model - Aug 8, 2017.
  
  Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.

  Deep Learning, LSTM, Machine Learning, NLP, Recurrent Neural Networks

• Machine Learning Algorithms: A Concise Technical Overview – Part 1 - Aug 4, 2017.
  
  These short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept.

  Algorithms, Machine Learning

• Train your Deep Learning Faster: FreezeOut - Aug 3, 2017.
  
  We explain another novel method for much faster training of Deep Learning models by freezing the intermediate layers, and show that it has little or no effect on accuracy.

  Deep Learning, Machine Learning, Model Performance, Modeling, Neural Networks

• The Machine Learning Abstracts: Decision Trees - Aug 3, 2017.
  
  Decision trees are a classic machine learning technique. The basic intuition behind a decision tree is to map out all possible decision paths in the form of a tree.

  Algorithms, Decision Trees, Machine Learning

• How to squeeze the most from your training data - Jul 27, 2017.
  
  In many cases, getting enough well-labelled training data is a huge hurdle for developing accurate prediction systems. Here is an innovative approach which uses SVM to get the most from training data.

  Data Analysis, Data Preparation, Machine Learning, Support Vector Machines, SVM, Training Data

• The Machine Learning Abstracts: Classification - Jul 27, 2017.
  
  Classification is the process of categorizing or “classifying” some items into a predefined set of categories or “classes”. It is exactly the same even when a machine does so. Let’s dive a little deeper.

  Algorithms, Classification, Machine Learning

• Machine Learning Exercises in Python: An Introductory Tutorial Series - Jul 26, 2017.
  
  This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.

  Andrew Ng, Machine Learning, Python

• AI and Deep Learning, Explained Simply - Jul 21, 2017.
  
  AI can now see, hear, and even bluff better than most people. We look into what is new and real about AI and Deep Learning, and what is hype or misinformation.
   

  Pages: 1 2 3

  AI, Deep Learning, Explained, Machine Learning

• Hacking in silico protein engineering with Machine Learning and AI, explained - Jul 19, 2017.
  
  Proteins are building blocks of all living matter. Although tremendous progress has been made, protein engineering remains laborious, expensive and truly complicated. Here is how Machine Learning can help.

  Bioinformatics, Machine Learning, Protein

• Road Lane Line Detection using Computer Vision models - Jul 19, 2017.
  
  A tutorial on how to implement a computer vision data pipeline for road lane detection used by self-driving cars.
   
   

  Pages: 1 2

  AI, Computer Vision, Data Science, Machine Learning, Python, Self-Driving Car

• Machine Learning Applied to Big Data, Explained - Jul 17, 2017.
  
  Machine learning with Big Data is, in many ways, different than "regular" machine learning. This informative image is helpful in identifying the steps in machine learning with Big Data, and how they fit together into a process of their own.

  Big Data, Explained, Machine Learning, Rubens Zimbres

• Why Every Company Needs a Digital Brain - Jul 11, 2017.
  
  As emerging technologies like AI/machine learning are adopted across different parts of the business, enterprises require a “digital brain” to coordinate those efforts and generate systemic intelligence.

  AI, Enterprise, H2O, Machine Learning

• What Are Artificial Intelligence, Machine Learning, and Deep Learning? - Jul 10, 2017.
  
  AI and Machine Learning have become mainstream, and people know shockingly little about it. Here is an explainer and useful references.

  AI, Artificial Intelligence, Deep Learning, Machine Learning, RapidMiner

• 5 Free Resources for Getting Started with Self-driving Vehicles - Jul 10, 2017.
  
  This is a short list of 5 resources to help newcomers find their bearings when learning about self-driving vehicles, all of which are free. This should be sufficient to learn the basics, and to learn where to look next for further instruction.

  Deep Learning, Machine Learning, Self-Driving Car, Udacity

• 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%)

  Feature Engineering, Jupyter, Kaggle, Machine Learning, Python

• Optimization in Machine Learning: Robust or global minimum? - Jun 30, 2017.
  
  Here we discuss how convex problems are solved and optimised in machine learning/deep learning.

  Deep Learning, Gradient Descent, Machine Learning, Optimization, UAI

• The world’s first protein database for Machine Learning and AI - Jun 22, 2017.
  
  dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp

  Bioinformatics, Genomics, Keras, Machine Learning, Protein, TensorFlow

• Making Sense of Machine Learning - Jun 21, 2017.
  
  Broadly speaking, machine learners are computer algorithms designed for pattern recognition, curve fitting, classification and clustering. The word learning in the term stems from the ability to learn from data.

  Machine Learning, Predictive Analytics, Supervised Learning, Unsupervised Learning

• Does Machine Learning Have a Future Role in Cyber Security? - Jun 20, 2017.
  
  In the past, ML learning hasn't had as much success in cyber security as in other fields. Many early attempts struggled with problems such as generating too many false positives, which resulted mixed attitudes towards it.

  Cybersecurity, Machine Learning

• The Machine Learning Algorithms Used in Self-Driving Cars - Jun 19, 2017.
  
  Machine Learning applications include evaluation of driver condition or driving scenario classification through data fusion from different external and internal sensors. We examine different algorithms used for self-driving cars.

  Algorithms, Boosting, Machine Learning, Self-Driving Car

• Understanding Deep Learning Requires Re-thinking Generalization - Jun 16, 2017.
  
  What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.

  Deep Learning, Machine Learning, Neural Networks

• 7 Ways to Get High-Quality Labeled Training Data at Low Cost - Jun 13, 2017.
  
  Having labeled training data is needed for machine learning, but getting such data is not simple or cheap. We review 7 approaches including repurposing, harvesting free sources, retrain models on progressively higher quality data, and more.

  Crowdsourcing, Data Preparation, Gamification, Machine Learning, Training Data

• Top 15 Python Libraries for Data Science in 2017 - Jun 13, 2017.
  
  Since all of the libraries are open sourced, we have added commits, contributors count and other metrics from Github, which could be served as a proxy metrics for library popularity.

  Pages: 1 2

  Data Mining, Data Science, Deep Learning, Machine Learning, Natural Language Processing, Python, Visualization

• The Practical Importance of Feature Selection - Jun 12, 2017.
  
  Feature selection is useful on a variety of fronts: it is the best weapon against the Curse of Dimensionality; it can reduce overall training times; and it is a powerful defense against overfitting, increasing generalizability.

  Feature Selection, Machine Learning, Rubens Zimbres

• The Unintended Consequences of Machine Learning - Jun 8, 2017.
  
  But with great power comes great responsibility. Let me tell you a story about the unintended consequences of well-meaning machine learning research.

  AI, Amazon, Machine Learning, Personalization

• 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!

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

• Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering - Jun 7, 2017.
  
  The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.

  Clustering, K-means, Machine Learning, Python, Workflow

• TPOT Automated Machine Learning Competition: Can AutoML beat humans on Kaggle? - Jun 5, 2017.
  
  Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.

  Automated Data Science, Automated Machine Learning, Competition, Machine Learning

• Is Regression Analysis Really Machine Learning? - Jun 5, 2017.
  
  What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?

  Applied Statistics, Linear Regression, Machine Learning, Regression, Statistics

• 7 Steps to Mastering Data Preparation with Python - Jun 2, 2017.
  
  Follow these 7 steps for mastering data preparation, covering the concepts, the individual tasks, as well as different approaches to tackling the entire process from within the Python ecosystem.

  Pages: 1 2

  7 Steps, Data Preparation, Data Preprocessing, Data Science, Data Wrangling, Machine Learning, Pandas, Python

• Which Machine Learning Algorithm Should I Use? - Jun 1, 2017.
  
  A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.

  Algorithms, Cheat Sheet, Machine Learning, Reinforcement Learning, Supervised Learning, Unsupervised Learning

• 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.

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

• Machine Learning Workflows in Python from Scratch Part 1: Data Preparation - May 29, 2017.
  
  This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation.

  Data Preparation, Machine Learning, Python, Workflow

• Machine Learning Anomaly Detection: The Ultimate Design Guide - May 25, 2017.
  
  Considering building a machine learning anomaly detection system for your high velocity business? Learn how with Anodot ultimate three-part guide.

  Anodot, Anomaly Detection, Machine Learning, Real-time

• Machine Learning Crash Course: Part 1 - May 24, 2017.
  
  This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.

  Classification, Cost Function, Gradient Descent, Machine Learning, Regression

• The Path To Learning Artificial Intelligence - May 19, 2017.
  
  Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.

  AI, Artificial Intelligence, Deep Learning, Learning Path, Machine Learning, Online Education, Python

• 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

• Data Version Control: iterative machine learning - May 11, 2017.
  
  ML modeling is an iterative process and it is extremely important to keep track of all the steps and dependencies between code and data. New open-source tool helps you do that.

  CRISP-DM, DVC, GitHub, Machine Learning, Open Source, Reproducibility, Version Control

• The Guerrilla Guide to Machine Learning with R - May 11, 2017.
  
  This post is a lean look at learning machine learning with R. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.

  Data Analysis, Machine Learning, R

• Top 10 Recent AI videos on YouTube - May 10, 2017.
  
  Top viewed videos on artificial intelligence since 2016 include great talks and lecture series from MIT and Caltech, Google Tech Talks on AI.

  AI, Google, Machine Learning, MIT, Neural Networks, NVIDIA, Robots, Youtube

• 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
  
  In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.

  Automated Machine Learning, Data Exploration, Deep Learning, Distributed Systems, Machine Learning, Overlook, Pandas, Reinforcement Learning

• Sales forecasting using Machine Learning - May 8, 2017.
  
  SpringML inviting business and sales leaders to its Man vs Machine Forecasting Duel - give them a day with your data and they will provide an algorithm based, unbiased forecast.

  Forecasting, Machine Learning, Sales, SpringML

• Data Science & Machine Learning Platforms for the Enterprise - May 8, 2017.
  
  A resilient Data Science Platform is a necessity to every centralized data science team within a large corporation. It helps them centralize, reuse, and productionize their models at peta scale.

  Algorithmia, Data Science Platform, Enterprise, Machine Learning

• Deep Learning in Minutes with this Pre-configured Python VM Image - May 5, 2017.
  
  Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.

  Deep Learning, Machine Learning, Python

• Machine Learning overtaking Big Data? - May 4, 2017.
  
  Is Machine Learning is overtaking Big Data?! We also examine trends for several more related and popular buzzwords, and see how BD, ML. Artificial Intelligence, Data Science, and Deep Learning rank.

  Big Data, Big Data Hype, Gartner, Google Trends, Machine Learning

• Top 10 Machine Learning Videos on YouTube, updated - May 3, 2017.
  
  The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.

  Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Machine Learning, Neural Networks, Robots, Video Games, Yaser Abu-Mostafa, Youtube

• How to Learn Machine Learning in 10 Days - May 1, 2017.
  
  10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.

  Machine Learning, Sebastian Raschka

• 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

• Cartoon: Machine Learning – What They Think I Do - Apr 29, 2017.
  
  Different views of Machine Learning: What society, my friends, my parents, other programmers think I do, and what I really do.

  Cartoon, Machine Learning

• AI & Machine Learning Black Boxes: The Need for Transparency and Accountability - Apr 25, 2017.
  
  When something goes wrong, as it inevitably does, it can be a daunting task discovering the behavior that caused an event that is locked away inside a black box where discoverability is virtually impossible.

  AI, Machine Learning, Transparency

• Data Science for the Layman (No Math Added) - Apr 20, 2017.
  
  Written for the layman, this book is a practical yet gentle introduction to data science. Discover key concepts behind more than 10 classic algorithms, explained with real-world examples and intuitive visuals.

  Book, Data Science, Machine Learning, Tutorial

• Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions - Apr 14, 2017.
  
  Who leads in Data Science, Machine Learning, and Predictive Analytics? We compare the latest Forrester and Gartner reports for this industry for 2017 Q1, identify gainers and losers, and strong leaders vs contenders.

  Data Science Platform, Forrester, Gartner, IBM, Knime, Machine Learning, Mike Gualtieri, Predictive Analytics, RapidMiner, SAS

• 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

• Machine Learning Finds “Fake News” with 88% Accuracy - Apr 12, 2017.
  
  In this post, the author assembles a dataset of fake and real news and employs a Naive Bayes classifier in order to create a model to classify an article as fake or real based on its words and phrases.

  Data Science, Fake News, Machine Learning, Naive Bayes, Politics, Text Analytics

• Must-Know: How to evaluate a binary classifier - Apr 11, 2017.
  
  Binary classification is a basic concept which involves classifying the data into two groups. Read on for some additional insight and approaches.

  Classifier, Interview Questions, Machine Learning

• 10 Free Must-Read Books for Machine Learning and Data Science - Apr 10, 2017.
  
  Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.

  Books, Data Science, ebook, Free ebook, Machine Learning

• Top 20 Recent Research Papers on Machine Learning and Deep Learning - Apr 6, 2017.
  
  Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".

  Deep Learning, Machine Learning, Research, Top list, Yoshua Bengio

• Top /r/MachineLearning Posts, March: A Super Harsh Guide to Machine Learning; Is it Gaggle or Koogle?!? - Apr 4, 2017.
  
  A Super Harsh Guide to Machine Learning; Google is acquiring data science community Kaggle; Suggestion by Salesforce chief data scientist; Andrew Ng resigning from Baidu; Distill: An Interactive, Visual Journal for Machine Learning Research

  Advice, Andrew Ng, Distill, Google, Kaggle, Machine Learning, Reddit, Salesforce

• Must-Know: Why it may be better to have fewer predictors in Machine Learning models? - Apr 4, 2017.
  
  There are a few reasons why it might be a better idea to have fewer predictor variables rather than having many of them. Read on to find out more.

  Feature Selection, Interview Questions, Machine Learning, Modeling

• What Top Firms Ask: 100+ Data Science Interview Questions - Mar 22, 2017.
  
  Check this out: A topic wise collection of 100+ data science interview questions from top companies.

  Algorithms, Data Science, Google, Hadoop, Interview Questions, Machine Learning, Microsoft, Statistics, Uber

• 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

• Applying Machine Learning To March Madness - Mar 16, 2017.
  
  March Madness is upon us. But before you get your brackets set, check out this overview of using machine learning to do the heavy lifting for you. A great discussion, and a timely topic.

  Basketball, Machine Learning, March Madness

• 50 Companies Leading The AI Revolution, Detailed - Mar 16, 2017.
  
  We detail 50 companies leading the Artificial Intelligence revolution in AD Sales, CRM, Autotech, Business Intelligence and analytics, Commerce, Conversational AI/Bots, Core AI, Cyber-Security, Fintech, Healthcare, IoT, Vision, and other areas.

  AI, Business Analytics, Cybersecurity, Data Science, Healthcare, IoT, Machine Learning

• Software Engineering vs Machine Learning Concepts - Mar 6, 2017.
  
  Not all core concepts from software engineering translate into the machine learning universe. Here are some differences I've noticed.

  Machine Learning, Software Engineering

• 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

• What I Learned Implementing a Classifier from Scratch in Python - Feb 28, 2017.
  
  In this post, the author implements a machine learning algorithm from scratch, without the use of a library such as scikit-learn, and instead writes all of the code in order to have a working binary classifier algorithm.

  Classification, Machine Learning, Perceptron, Python, Sebastian Raschka

• Machine Learning-driven Firewall - Feb 23, 2017.
  
  Cyber Security is always a hot topic in IT industry and machine learning is making security systems more stronger. Here, a particular use case of machine learning in cyber security is explained in detail.

  Firewall, Fsecurify, GitHub, Machine Learning, Security

• Stacking Models for Improved Predictions - Feb 21, 2017.
  
  This post presents an example of regression model stacking, and proceeds by using XGBoost, Neural Networks, and Support Vector Regression to predict house prices.

  Ensemble Methods, Machine Learning, XGBoost

• Apache Arrow and Apache Parquet: Why We Needed Different Projects for Columnar Data, On Disk and In-Memory - Feb 16, 2017.
  
  Apache Parquet and Apache Arrow both focus on improving performance and efficiency of data analytics. These two projects optimize performance for on disk and in-memory processing

  Apache, Apache Arrow, Apache Spark, Data Science, Dremio, In-Memory Computing, Machine Learning, Python

• 5 Career Paths in Big Data and Data Science, Explained - Feb 6, 2017.
  
  Sexiest job... massive shortage... blah blah blah. Are you looking to get a real handle on the career paths available in "Data Science" and "Big Data?" Read this article for insight on where to look to sharpen the required entry-level skills.

  Big Data, Career, Data Analyst, Data Engineering, Data Infrastructure, Data Science, Explained, Machine Learning