- 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.
- Fidelity Investments: Vice President (AI Lead) – Artificial Intelligence, Machine Learning & Big Data - Jul 7, 2017.
Seeking an outstanding hands-on AI leader who can partner with business stakeholders and identify/prioritize top AI opportunities, create business/technical requirements, transform large volumes of data into AI-driven solutions using creative, and lead ML strategy and road map planning.
- 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%)
- Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!? - Jul 3, 2017.
NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE
- 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.
- Why Artificial Intelligence and Machine Learning? - Jun 30, 2017.
With your goals (i.e., the why) in mind, the next step for any artificial intelligence or machine learning solution is to specify how (e.g., which algorithms or models to use) to achieve a specific goal or set of goals, and finally what the end result will be (e.g., product, report, predictive model).
- Interesting Things Learned as a Student of Machine Learning - Jun 29, 2017.
Did you ever learn something you didn't really want to? The path to machine learning mastery is paved with such collateral knowledge. Here are a few examples of such information I have gleaned while trekking away.
- KDnuggets™ News 17:n25, Jun 28: Emerging Data Science Software Ecosystem; 3 Key Data Science 2017 Hiring Trends - Jun 28, 2017.
Emerging Data Science Software Ecosystem; 3 Key Trends Shaping the 2017 Data Science Hiring Market; Top 10 Quora Machine Learning Writers and Their Best Advice; The world's first protein database for Machine Learning and AI; Making Sense of Machine Learning
- How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part 2 - Jun 27, 2017.
In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles.
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- Top 10 Quora Machine Learning Writers and Their Best Advice, Updated - Jun 26, 2017.
Gain some insight on a variety of topics with select answers from Quora's current top machine learning writers. Advice on research, interviews, hot topics in the field, how to best progress in your learning, and more are all covered herein.
- 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
- Top KDnuggets tweets, Jun 14-20: 5 EBooks to Read Before Getting into A Data Science or Big Data Career - Jun 21, 2017.
Also 10 Free Must-Read Books for #MachineLearning and #DataScience; #Keras implementation of a simple Neural Net module for relational reasoning; Applying #deeplearning to real-world problems
- 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.
- 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.
- Best Data Science Courses from Udemy (only $10 till June 21) - Jun 19, 2017.
Here are some of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until June 21, 2017.
- 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.
- 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.
- Top KDnuggets tweets, Jun 07-13: Is Regression Analysis Really Machine Learning? - Jun 14, 2017.
Machine Learning in Real Life: Tales from the Trenches; Is Regression Analysis Really Machine Learning?; Implementing Your Own k-Nearest Neighbour Algorithm Using Python; Building Simple Neural Networks - TensorFlow for Hackers.
- Open Innovation and Crowdsourcing in Machine Learning – Getting premium value out of data - Jun 14, 2017.
Recently, PSL Research University launched a one-week course combining theoretical lectures and practical sessions. 115 students from various backgrounds and skill levels were enrolled; something quite spectacular happened during the week: Students have achieved an astounding level of score improvement - in just three afternoons.
- KDnuggets™ News 17:n23, Jun 14: The Practice of Machine Learning, Data Science Implementation, and Feature Selection - Jun 14, 2017.
A Practical Guide to Machine Learning; Your Checklist to Get Data Science Implemented in Production; The Practical Importance of Feature Selection; Machine Learning in Real Life: Tales from the Trenches.
- 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.
- 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.
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- 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.
- Autonomous Vehicles Need Superhuman Perception for Success - Jun 12, 2017.
Michael Milford, Associate Professor at Queensland University of Technology (QUT), is a leading robotics researcher working to improve perception and more in autonomous vehicles, conducting his research at the intersection of robotics, neuroscience and computer vision.
- Top /r/MachineLearning Posts, May: Deep Image Analogy; Stylized Facial Animations; Google Open Sources Sketch-RNN - Jun 9, 2017.
Deep Image Analogy; Example-Based Synthesis of Stylized Facial Animations; Google releases dataset of 50M vector drawings, open sources Sketch-RNN implementation; New massive medical image dataset coming from Stanford; Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?
- A Practical Guide to Machine Learning: Understand, Differentiate, and Apply - Jun 9, 2017.
So, if Machine Learning was first defined in 1959, why is this now the time to seize the opportunity? It’s the economics.
- New Speakers Announced for the European Machine Intelligence Summit & Machine Intelligence in Autonomous Vehicles Summit in Amsterdam, 28-29 June - Jun 8, 2017.
Explore the cutting-edge technology leading the way in Machine Intelligence and Autonomous Vehicles and it’s applications in industry at the Amsterdam Summits on June 28th & 29th. Use the discount code KDNUGGETS to save 20% on all tickets.
- 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.
- 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!
- Machine Learning in Real Life: Tales from the Trenches to the Cloud – Part 1 - Jun 8, 2017.
We live in a world where everyone knows enough about the Buzzwords “Deep Learning” and “Big Data”... we also live in a world where if you’re a developer you can, while knowing nothing about machine learning, go from zero to training a OCR model in the space of an hour.
- 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.
- KDnuggets™ News 17:n22, Jun 7: 7 Steps to Mastering Data Preparation with Python; Why Does Deep Learning Not Have a Local Minimum? - Jun 7, 2017.
7 Steps to Mastering Data Preparation with Python; Why Does Deep Learning Not Have a Local Minimum?; 7 Techniques to Handle Imbalanced Data; Which Machine Learning Algorithm Should I Use?; Is Regression Analysis Really Machine Learning?
- DataRobot Webinar on June 27, 2017: Automated Machine Learning in Action - Jun 6, 2017.
In this webinar, learn how DataRobot automates predictive modeling, and how our platform can deliver these same types of insights and a substantial productivity boost to your machine learning endeavors.
- 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.
- 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?
- 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.
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- 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.
- 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.
- KDnuggets™ News 17:n21, May 31: Python Machine Learning Workflows from Scratch; Machine Learning Crash Course - May 31, 2017.
Machine Learning Workflows in Python from Scratch Part 1: Data Preparation; Machine Learning Crash Course: Part 1; An Introduction to the MXNet Python API; How A Data Scientist Can Improve Productivity; Data science platforms are on the rise and IBM is leading the way
- Challenges in Machine Learning for Trust - May 29, 2017.
With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
- 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.
- 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.
- ExxonMobil: Machine Learning - May 24, 2017.
Seeking a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section.
- 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.
- 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.
- Webinar: A New Era of Data Science – Unlocking Big Data Insights with Machine Learning and Spark, May 31 - May 19, 2017.
Learn about Big Data technologies and trends, Democratizing Big Data analytics, Big Data and the Cloud, and more in this webcast with top experts Dean Abbott and Mamdouh Refaat.
- Data Preparation Strategies for Successful Machine Learning - May 18, 2017.
This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.
- Top KDnuggets tweets, May 10-16: Which Machine Learning algorithm should I use? #cheatsheet - May 17, 2017.
Also HDFS vs. HBase: All you need to know #BigData mini-tutorial; #MachineLearning overtaking #BigData?
- Best Data Science Courses from Udemy (only $10 till May 27) - May 17, 2017.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until May 27, 2017.
- KDnuggets™ News 17:n19, May 17: Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can’t Overlook - May 17, 2017.
The Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can No Longer Overlook, May; The Two Phases of Gradient Descent in Deep Learning; HDFS vs. HBase: All you need to know; Must-Know: What are common data quality issues for Big Data and how to handle them?
- 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.
- 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.
- 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.
- 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.
- 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.
- KDnuggets™ News 17:n18, May 10: KDnuggets Poll: Software used for Analytics, Data Science? Top Machine Learning videos - May 10, 2017.
Also Top 10 Machine Learning Videos on YouTube, updated; Deep Learning in Minutes with this ...; Machine Learning overtaking Big Data?
- MLTrain: transitioning academic theory to practice - May 9, 2017.
Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.
- 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.
- 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.
- 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.
- Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow - May 5, 2017.
Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI
- 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.
- 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.
- KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified - May 3, 2017.
How to Learn Machine Learning in 10 Days; Keep it simple! How to understand Gradient Descent algorithm; The Guerrilla Guide to Machine Learning with Python; What Data You Analyzed - KDnuggets Poll Results and Trends; Cartoon: Machine Learning - What They Think I Do
- 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.
- 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.
- 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.
- Resource-aware Machine Learning – International Summer School, Sep 25-28, TU Dortmund - Apr 27, 2017.
How to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? Learn at TU Dortmund International Summer School. Apply by July 15.
- Top KDnuggets tweets, Apr 19-25: 10 Free Must-Read Books for Machine Learning and Data Science - Apr 26, 2017.
Also Practical #DeepLearning For Coders-18 hours of free lessons; Different views of #Machinelearning #cartoon #humor; Scikit-learn #MachineLearning classification algorithms.
- 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.
- Best Data Science Courses from Udemy (only $10 till Apr 29) - Apr 24, 2017.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until April 29, 2017.
- 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.
- KDnuggets™ News 17:n15, Apr 19: Forrester vs Gartner on Data Science/Analytics Platforms; 5 Machine Learning Projects You Can No Longer Overlook - Apr 19, 2017.
Also Top mistakes data scientists make when dealing with business people; New Online Data Science Tracks for 2017; Cartoon: Why AI needs help with taxes.
- Sift Science: Sr. Software Engineer, Machine Learning - Apr 17, 2017.
Seeking an experienced engineer who has built scalable machine learning data pipelines & systems, and feels equally comfortable running small experiments on their laptop using R as they do running Spark or Map Reduce jobs on remote clusters.
- 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.
- 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.
- 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.
- KDnuggets™ News 17:n14, Apr 12: Free Machine Learning & Data Science Books; Top Machine Learning & Deep Learning Papers - Apr 12, 2017.
10 Free Must-Read Books for Machine Learning and Data Science; Top 20 Recent Research Papers on Machine Learning and Deep Learning; New Poll: What data types you analyzed?; The 42 V's of Big Data and Data Science; A Brief History of Artificial Intelligence
- 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.
- 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.
- ODSC East 2017: Accelerate your Deep Learning & Machine Learning Knowledge and Career Opportunities, Boston, May 3-5 - Apr 7, 2017.
With change comes opportunity! The pace of data science innovation continues to quicken. New tools and techniques are constantly emerging especially around deep learning and machine learning. In 2017 many more companies are embarking on data science projects.
- 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".
- KDnuggets™ News 17:n13, Apr 5: What makes a great data scientist? Best R Packages for Machine Learning - Apr 5, 2017.
Also Best R Packages for Machine Learning; Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Learning; A Short Guide to Navigating the Jupyter Ecosystem.
- 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
- 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.
- The Best R Packages for Machine Learning - Mar 30, 2017.
There is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages.
- From Big Data Platforms to Platform-less Machine Learning - Mar 27, 2017.
The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.
- 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.
- 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.
- 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.
- 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.
- Data Science Game, Machine learning competition for students - Mar 14, 2017.
Improve your skills and have fun with other talented students from all around the world. Reg by April 9, online qualification ends May 31, and final phase in Paris, Fall 2017.
- Best Data Science Courses from Udemy (only $19 till Mar 31) - Mar 10, 2017.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $19 until March 31, 2017.
- The HPI Future SOC Lab offers researchers free access to a powerful Big Data infrastructure - Mar 10, 2017.
The HPI Future SOC (Service-Oriented Computing) Lab is a cooperation of the Hasso Plattner Institute (HPI) and industrial partners, providing free access to a powerful Big Data & Computing infrastructure. It is now accepting project proposals for 2017.
- Free Online Books Explaining Big Data, Machine Learning, Blockchain and More - Mar 10, 2017.
It has been a challenge to keep up-to-date with new concepts from NoSQL, to machine learning, to Internet of things and blockchain, but Little Bee Books is here with free solutions to helping you do so.
- KDnuggets™ News 17:n09, Mar 8: 7 More Steps to Mastering Machine Learning w. Python; Every Intro to Data Science Course, Ranked - Mar 8, 2017.
Also The Data Science Project Playbook; Hadoop Is Falling - Why? Bokeh Cheat Sheet: Data Visualization in Python
- 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.
- 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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- Cooperative Trust Among Neural Networks Drives Deeper Learning - Feb 28, 2017.
Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.
- 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.
- The 6 Best Data Science Courses from Udemy (only $10 till Feb 28) - Feb 25, 2017.
Here a list of the best courses in data science from Udemy, covering Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Feb 28, 2017.
- Machine 4.0: Making your Factory, Production and Maintenance Data Work - Feb 24, 2017.
To leverage the potential of Big Data the manufacturing firms should intelligently integrate and connect their data sources on a unified platform and use machine learning to extract insights, analyze them, and derive results.
- 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.
- The Gentlest Introduction to Tensorflow – Part 4 - Feb 22, 2017.
This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.
- The Gentlest Introduction to Tensorflow – Part 3 - Feb 21, 2017.
This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.
- 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.
- AI & Machine Learning World, London, 13-15 June 2017 – KDnuggets Offer - Feb 21, 2017.
AI & Machine Learning World, part of London Tech Week, brings together global thought leaders who have driven the adoption of machine learning within global enterprises. Use code TEC6245KD to save.
- 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
- KDnuggets™ News 17:n06, Feb 15: So What is Big Data? 52 Useful Machine Learning APIs; Data Science finds Perfect Valentines Dates - Feb 15, 2017.
Also Making Python Speak SQL with pandasql; 52 Useful Machine Learning & Prediction APIs, updated; New Poll: Do you support Trump Immigration Ban?
- FeatureX: Software Engineer - Feb 10, 2017.
Seeking a software engineer, responsible for the design and development of machine learning and computer vision platforms and data systems.
- FeatureX: Machine Learning Research Scientist - Feb 10, 2017.
As a machine learning research scientist, you will be developing machine learning techniques for a wide variety of data sources, ranging from financial time series data to features extracted from satellite imagery.
- 50+ Useful Machine Learning & Prediction APIs, updated - Feb 8, 2017.
Very useful, updated list of 50+ APIs in machine learning, prediction, text analytics & classification, face recognition, language translation, and more.
- KDnuggets™ News 17:n05, Feb 8: Identifying Better Predictors; 5 Career Paths in Big Data, Data Science Explained - Feb 8, 2017.
Identifying Variables That Might Be Better Predictors; 5 Career Paths in Big Data and Data Science, Explained; 5 Free Courses for Getting Started in Artificial Intelligence; 3 practical thoughts on why deep learning performs so well
- Top /r/MachineLearning Posts, January: TensorFlow Updates; AlphaGo in the Wild; Self-Driving Mario Kart - Feb 7, 2017.
TensorFlow 1.0.0-alpha; Unknown bot repeatedly beats top Go players online - so far it's undefeated; TensorKart: self-driving MarioKart with TensorFlow; GTA V integration into Universe is now open-source; Keras will be added to core TensorFlow at Google
- 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.
- Top R Packages for Machine Learning - Feb 3, 2017.
What are the most popular ML packages? Let's look at a ranking based on package downloads and social website activity.
- Learning to Learn by Gradient Descent by Gradient Descent - Feb 2, 2017.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
- Top KDnuggets tweets, Jan 25-31: Python implementations of Andrew Ng #MachineLearning MOOC exercises - Feb 1, 2017.
#Python implementations of Andrew Ng #MachineLearning MOOC exercises; This repository contains the entire #Python #DataScience Handbook; What are the best #visualizations of #MachineLearning algorithms? Learn #TensorFlow and #DeepLearning, without a PhD.
- Is Deep Learning the Silver Bullet? - Feb 1, 2017.
With nearly every every smart young computer scientist planning to work on deep learning, are there really still artificial intelligence researchers working on other techniques? Is deep learning the AI silver bullet?
- KDnuggets™ News 17:n04, Feb 1: Data Science and Python Wrangling: Pandas Cheat Sheet; Great Collection of Machine Learning Algorithms - Feb 1, 2017.
Also Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms; Bad Data + Good Models = Bad Results; Data Scientist - best job in America, again.
- 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
- Great Collection of Minimal and Clean Implementations of Machine Learning Algorithms - Jan 25, 2017.
Interested in learning machine learning algorithms by implementing them from scratch? Need a good set of examples to work from? Check out this post with links to minimal and clean implementations of various algorithms.
- KDnuggets™ News 17:n03, Jan 25: Automated Machine Learning Overview; Data Science Puzzle; Chatbots on Steroids - Jan 25, 2017.
The Current State of Automated Machine Learning; The Data Science Puzzle, Revisited; Chatbots on Steroids; Data Science of Sales Calls: 3 Actionable Findings; Four Problems in Using CRISP-DM and How To Fix Them
- The Top Predictive Analytics Pitfalls to Avoid - Jan 23, 2017.
Predictive modelling and machine learning are significantly contributing to business, but they can be very sensitive to data and changes in it, which makes it very important to use proper techniques and avoid pitfalls in building data science models.
- Chatbots on Steroids: 10 Key Machine Learning Capabilities to Fuel Your Chatbot - Jan 23, 2017.
As chatbots become a common practice, the need for smarter bots arises. Empowering your bot with machine learning capabilities can really differentiate it from the rest. Check out these 10 capabilities to help fuel your chatbot.
- The Data Science Puzzle, Revisited - Jan 20, 2017.
The data science puzzle is re-examined through the relationship between several key concepts in the realm, and incorporates important updates and observations from the past year. The result is a modified explanatory graphic and rationale.
- Data Science of Sales Calls: 3 Actionable Findings - Jan 19, 2017.
How does AI help sales and marketing teams in the organisation? Let’s understand Dos and don’ts of sales calls with the help of analysis of over 70,000+ B2B SaaS sales calls.
- The Current State of Automated Machine Learning - Jan 18, 2017.
What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.
- KDnuggets™ News 17:n02, Jan 18: Most Popular Language For Machine Learning; Analytics & Data Science Make Business Smarter - Jan 18, 2017.
The Most Popular Language For Machine Learning and Data Science; Analytics & Data Science Make Business Smarter; Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science; 90 Active Blogs on Analytics, Big Data, Data Mining, and Data Science