- 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.
- KDnuggets™ News 17:n35, Sep 13: Putting the “Science” Back in Data Science; Python vs. R: And the leader is… - Sep 13, 2017.
Putting the "Science" Back in Data Science; Python vs R - Who Is Really Ahead in Data Science, Machine Learning; I built a chatbot in 2 hours and this is what I learned; Are Data Lakes Fake News?; Python Overtaking R?
- 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.
- 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.
- 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.
- Python vs R for Artificial Intelligence, Machine Learning, and Data Science - Sep 11, 2017.
This is a summary (with links) of a three-part article series that's intended to be an in-depth overview of the considerations, tradeoffs, and recommendations associated with selecting between Python and R for programmatic data science tasks.
- Top KDnuggets tweets, Aug 30 – Sep 5: Python overtakes R, becomes the leader in #DataScience; Humble Book Bundle: #DataScience - Sep 6, 2017.
Also: Pandas tips and tricks #Python #DataScience; How I replicated an $86 million project in 57 lines of code; Future #MachineLearning Class.
- 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.
- KDnuggets™ News 17:n34, Sep 6: 277 Data Science Key Terms, Explained; Top 10 Machine Learning Use Cases; Future Machine Learning Class - Sep 6, 2017.
Also Top 10 Machine Learning Use Cases; Search Millions of Documents for Thousands of Keywords in a flash; Cartoon: Future Machine Learning Class; Data Science: (not) the preferred nomenclature.
- Learn from experts at Netflix, Facebook, Tesla, DeepMind & more - Sep 5, 2017.
January 25 & 26 in San Francisco will see the sixteenth global Deep Learning Summit and the fifth global AI Assistant Summit joined by the first ever Deep Learning for Enterprise Summit. Use code KDNUGGETS to save 20% on Early Bird passes!
- 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.
- Cartoon: Future Machine Learning Class - Sep 2, 2017.
New KDnuggets Cartoon looks at an unusual but possible future Machine Learning Class.
- 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.
- 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.
- 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.
- KDnuggets™ News 17:n33, Aug 30: Python Overtakes R in Machine Learning; Data Science in 42 Steps; Deep Learning not AI’s Future - Aug 30, 2017.
Also: KDnuggets part-time, paid internship in Data Science/Machine Learning Journalism; How To Write Better SQL Queries: The Definitive Guide; Understanding overfitting: an inaccurate meme in Machine Learning; How to Become a Data Scientist: The Definitive Guide
- 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.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- A Guide to Understanding AI Toolkits - Aug 16, 2017.
This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
- KDnuggets™ News 17:n31, Aug 16: Data Science Primer: Basic Concepts; Python vs R vs rest - Aug 16, 2017.
Also: What Artificial Intelligence and Machine Learning Can Do-And What It Can't; How Convolutional Neural Networks Accomplish Image Recognition?; Making Predictive Models Robust: Holdout vs Cross-Validation; The Machine Learning Abstracts: Support Vector Machines
- 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.
- DataRobot: The Making of Data Science Superheroes, Webinar August 31 - Aug 11, 2017.
Learn how two data scientists quickly transformed from mere mortals into data science superheroes, now able to tackle more projects with better results - faster than a speeding bullet!
- Transforming from Autonomous to Smart: Reinforcement Learning Basics - Aug 11, 2017.
This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
- 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?
- The Machine Learning Abstracts: Support Vector Machines - Aug 10, 2017.
While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).
- Top /r/MachineLearning Posts, July: Friendly Suggestions re: Coding Practices; Racist AI How-To Without Really Trying - Aug 10, 2017.
Why can't you guys comment your f*cking code?; Train Chrome's Trex character to play independently; How to make a racist AI without really trying; Is training a NN to mimic a closed-source library legal?; 37 Reasons why your NN is not working
- Top KDnuggets tweets, Aug 2-8: PyTorch: concise overview of the framework and its tensor implementation - Aug 9, 2017.
Also: What is the most important step in a #MachineLearning project? #MachineLearning Algorithms: a concise technical overview; McKinsey state of #MachineLearning and #AI.
- KDnuggets™ News 17:n30, Aug 9: Machine Learning Algorithms: Concise Overview; Train your Deep Learning model faster and sharper - Aug 9, 2017.
Also: A unified deep learning framework for time-series mobile sensing data processing; EDISON Data Science Framework Release 2; Data mining Airbnb.
- Strata Data Conference, the reunion of data brain trust – KDnuggets Offer - Aug 8, 2017.
Strata Data Conference, the annual reunion of data brain trust, is Sept 25-28 in New York. Early price ends Aug 11 - save more with code KDNU.
- 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.
- Best Data Science, Machine Learning Courses from Udemy (only $10 or $12 till Aug 10) - Aug 6, 2017.
Back-to-school sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 or $12 until Aug 10, 2017.
- 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.
- 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.
- 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.
- Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python - Aug 2, 2017.
Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
- Train your Deep Learning model faster and sharper: Snapshot Ensembling — M models for the cost of 1 - Aug 2, 2017.
We explain a novel Snapshot Ensembling method for increasing accuracy of Deep Learning models while also reducing training time.
- KDnuggets™ News 17:n29, Aug 2: Machine Learning Exercises in Python; 8 Reasons Why Many Big Data Analytics Solutions Fail - Aug 2, 2017.
Machine Learning Exercises in Python: An Introductory Tutorial Series; The BI & Data Analysis Conundrum: 8 Reasons Why Many Big Data Analytics Solutions Fail to Deliver Value; The Internet of Things: An Introductory Tutorial Series; How to squeeze the most from your training data
- 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.
- 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.
- Machine Learning and Misinformation - Jul 27, 2017.
The creative aspects of machine learning are overshadowed by visions of an autonomous future, but machine learning is a powerful tool for communication. Most machine learning in today’s products is related to understanding.
Pages: 1 2
- 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.
- ExxonMobil: Machine Learning Position - Jul 21, 2017.
Seeking a full-time staff position in the area of machine learning in our Data Analytics and Optimization Section.
- 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
- Deep Learning, AI Assistant Summits London feature DeepMind and much more, Sep 21-22 – KDnuggets Offer - Jul 20, 2017.
The Deep Learning Summit London and the AI Assistant Summit London will be continuing the RE•WORK Global Summit Series this September 21 & 22. Early Bird discount is ending on July 28th. Register now to guarantee a spot at the Summit and use the discount code KDNUGGETS to save 20% on all tickets.
- 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.
- 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
- KDnuggets™ News 17:n27, Jul 19: The 4 Types of Data Analytics; Machine Learning Applied to Big Data, Explained - Jul 19, 2017.
The 4 Types of Data Analytics; Machine Learning Applied to Big Data, Explained; Are Most Machine Learning Experts Turning to Deep Learning?; How to Build a Data Science Pipeline; Cartoon: The First Ever Self-Driving, Deep Learning Grill
- Are Most Machine Learning Experts Turning to Deep Learning? - Jul 18, 2017.
Read a short opinion on what the impact of machine learning researchers focusing on deep learning will be.
- Optimizing Web sites: Advances thanks to Machine Learning - Jul 17, 2017.
Machine learning has revitalized a nearly dormant method, leading to a powerful approach for optimizing Web pages, finding the best of thousands of alternatives.
Pages: 1 2
- 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.
- DataRobot: Become a Data Science SuperHero, Webinar, July 25 - Jul 14, 2017.
DataRobot machine learning automation platform transforms you from mild-mannered to superhuman in your abilities to develop and deploy highly-accurate predictive models. Learn more in this webinar.
- Stylight: Machine Learning Engineer - Jul 13, 2017.
We are looking for an experienced Machine Learning Engineer who wants to liberate us from manual classification. We use English as our main language.
- Automated Machine Learning — A Paradigm Shift That Accelerates Data Scientist Productivity - Jul 13, 2017.
There is a growing community around creating tools that automate many machine learning tasks, as well as other tasks that are part of the machine learning workflow. The paradigm that encapsulates this idea is often referred to as automated machine learning.
- Top KDnuggets tweets, Jul 05-11: 10 Free Must-Read Books for #MachineLearning and #DataScience; Why AI and Machine Learning? - Jul 12, 2017.
Also great overview: Unintuitive properties of #NeuralNetworks; #Apache #Flink vs #Spark: The Strange Loop in #DeepLearning - the coolest idea in #MachineLearning in 20 yrs;
- The Guerrilla Guide to Machine Learning with Julia - Jul 12, 2017.
This post is a lean look at learning machine learning with Julia. It is a complete, if very short, course for the quick study hacker with no time (or patience) to spare.
- 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.
- 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.
- 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.
Pages: 1 2
- 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.
Pages: 1 2
- 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.
Pages: 1 2
- 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.