Category Archives: Machine Learning
Azure Machine Learning Studio R Runtime Upgrade
Aired on October 31, 2018
The R language engine in the Execute R Script module of Azure Machine Learning Studio has added a new R runtime version -- Microsoft R Open (MRO) 3.4.4. MRO 3.4.4 is based on open-source CRAN R 3.4.4 and is therefore compatible with packages that works with that version of R.
Mining Campaign Funds
Aired on August 03, 2017
Play with 2016 Presidential Campaign finance data while learning how to prepare a large dataset for machine learning by processing and engineering features. This sample experiment works on a 2.5 GB dataset and will take about 20 minutes to run in its entirety.
Inside the Data Science VM
Aired on June 21, 2016
DSVM is a custom Azure Virtual Machine image that is published on the Azure marketplace and available on both Windows and Linux. It contains several popular data science and development tools both from Microsoft and from the open source community all pre-installed and pre-configured and ready to use. We will cover best practices that would show how you can use the DSVM effectively to run your next data science or analytics project.
Originally posted here:
Typing what is machine learning? into a Google search opens up a pandoras box of forums, academic research, and here-say and the purpose of this article is to simplify the definition and understanding of machine learning thanks to the direct help from our panel of machine learning researchers.
In addition to an informed, working definition of machine learning (ML), we aim toprovide a succinct overview of the fundamentals of machine learning, the challenges and limitations of getting machine to think, some of the issues being tackled today in deep learning (the frontier of machine learning), and key takeaways for developingmachine learningapplications.
This article will be broken up into the following sections:
We put together this resource to help with whatever your area of curiosity about machine learning so scroll along to your section of interest, or feel free to read the article in order, starting with our machine learning definition below:
* Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.
The above definition encapsulates the ideal objective or ultimate aim of machine learning, as expressed by many researchers in the field. The purpose of this article is to provide a business-minded reader with expert perspective on how machine learning is defined, and how it works.Machine learning and artificial intelligence share the same definition in the minds of many however, there are some distinct differences readers should recognize as well. References and related researcher interviews are included at the end of this article for further digging.
(Our aggregate machine learning definition can be found at the beginning of this article)
As with any concept, machine learning may have a slightly different definition, depending on whom you ask. We combed the Internet to find five practicaldefinitions from reputable sources:
We sent these definitions to experts whom weve interviewed and/or included in one of our past research consensuses, and asked them to respond with their favorite definition or to provide their own. Our introductory definition is meant to reflect the varied responses. Below are someof their responses:
Dr. Yoshua Bengio,Universit de Montral:
ML should not be defined by negatives (thus ruling 2 and 3). Here is my definition:
Machine learning research is part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. That acquired knowledge allows computers to correctly generalize to new settings.
Dr. Danko Nikolic, CSC and Max-Planck Institute:
(edit of number 2 above): Machine learning is the science of getting computers to act without being explicitly programmed, but instead letting them learn a few tricks on their own.
Dr. Roman Yampolskiy, University ofLouisville:
Machine Learning is the science of getting computers to learn as well as humans do or better.
Dr. Emily Fox, University of Washington:
My favorite definition is #5.
There are many different types of machine learning algorithms, with hundreds published each day, and theyretypically grouped by either learning style (i.e. supervised learning, unsupervised learning, semi-supervised learning) or by similarity in form or function (i.e. classification, regression, decision tree, clustering, deep learning, etc.). Regardless of learning style or function, all combinations of machine learning algorithms consist of the following:
Image credit: Dr. Pedro Domingo, University of Washington
The fundamental goal of machine learning algorithms is togeneralize beyond the training samples i.e. successfully interpret data that it has never seen before.
Concepts and bullet points can only take one so far in understanding.When people ask What is machine learning?, they often want to see what it is and what it does. Below are some visual representations of machine learning models, with accompanying links for further information. Even more resources can be found at the bottom of this article.
Decision tree model
Gaussian mixture model
Dropout neural network
Merging chrominance and luminance using Convolutional Neural Networks
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task youre trying to accomplish and the type and amount of data that you have available. This dynamic sees itself played out in applications as varyingas medical diagnostics or self-driving cars.
While emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par. Most of the time this is a problem with training data, but this also occurs when working with machine learning in new domains.
Research done when working on real applications often drives progress in the field, and reasons are twofold: 1. Tendency to discover boundaries and limitations of existing methods 2. Researchers and developers working with domain experts andleveraging time and expertise to improve system performance.
Sometimes this also occurs by accident. We might consider model ensembles, or combinations of many learning algorithms to improve accuracy, to be one example. Teams competing for the 2009 Netflix Price found that they got their best results when combining their learners with other teams learners, resulting in an improved recommendation algorithm (read Netflixs blog for more on why theydidnt end up using this ensemble).
One important point (based on interviews and conversations with experts in the field), in terms of application within business and elsewhere, is that machine learning is not just, or even about, automation, an often misunderstood concept. If you think this way, youre bound to miss the valuable insights that machines can provide and the resulting opportunities (rethinking an entire business model, for example, as has been in industries like manufacturing and agriculture).
Machines that learn are useful to humans because, with all of their processing power, theyre able to more quickly highlight or find patterns in big (or other) data that would have otherwise been missed by human beings. Machine learning is a tool that can be used to enhance humans abilities to solve problems and make informed inferences on a wide range of problems, from helping diagnose diseases to coming up with solutions for global climate change.
Machine learning cant get something from nothingwhat it does is get more from less. Dr. Pedro Domingo, University of Washington
The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e. learns random things when trained on new data) and dimensionality (algorithms with more features work in higher/multiple dimensions, making understanding the data more difficult). Having access to a large enough data set has in some cases also been a primary problem.
One of the most common mistakes among machine learning beginners is testing training data successfully and having the illusion of success; Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning on the whole data set.
When a learning algorithm (i.e. learner) is not working, often the quicker path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in machine and deep learning algorithms in recent years; however, this can lead to issues with scalability, in which we have more data but time to learn that data remains an issue.
In terms of purpose, machine learning is not an end or a solution in and of itself. Furthermore, attempting to use it as a blanket solution i.e. BLANKis not a useful exercise; instead, coming to the table with a problem or objective is often best driven bya more specific question BLANK.
Deep learning involves the study and design of machine algorithms for learning good representation of data at multiple levels of abstraction (ways of arranging computer systems). Recent publicity of deep learning through DeepMind, Facebook, and other institutionshas highlighted it as the next frontier of machine learning.
The International Conference on Machine Learning (ICML) is widely regarded as one of the most important in the world. This years took place in June in New York City, and it brought together researchers from all over the world who are working on addressing the current challenges in deep learning:
Deep-learning systems have made great gains over the past decade in domains like bject detection and recognition, text-to-speech, information retrieval and others. Research is now focused on developingdata-efficient machine learning i.e. deep learning systems that can learn more efficiently, with the same performance in less time and with less data, in cutting-edge domains like personalized healthcare, robot reinforcement learning, sentiment analysis, and others.
Below is a selection of best-practices and concepts of applying machine learning that weve collated from our interviews for out podcast series, and from select sources cited at the end of this article. We hope that some of these principles will clarify how ML is used, and how to avoid some of the common pitfalls that companies and researchers might be vulnerable to in starting off on an ML-related project.
One of the best ways to learn about artificial intelligence concepts is to learn from the research and applications of the smartest minds in the field. Below is a brief list of some of our interviews with machine learning researchers, many of which may be of interest for readers who want to explore these topics further:
Read more here:
These are the Step-by-Step Guides that YouveBeen Looking For!What do you want help with?
How Do I Get Started?
The most common question Im asked is: how do I get started?
My best advice for getting started in machine learning is broken down into a 5-step process:
For more on this top-down approach, see:
Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists.
Applied Machine Learning Process
The benefit of machine learning are the predictions and the models that make predictions.
To have skill at applied machine learning means knowing how to consistently and reliably deliver high-quality predictions on problemafter problem. You need to follow a systematic process.
Below is a 5-step process that you can follow to consistently achieve above average results on predictive modeling problems:
For a good summary of this process, see the posts:
Linear algebra is an important foundation area of mathematics required for achieving a deeper understanding of machine learning algorithms.
Below is the 3 step process that you can use to get up-to-speed with linear algebra for machine learning, fast.
You can see all linear algebra posts here. Below is a selection of some of the most popular tutorials.
Statistical Methods an important foundation area of mathematics required for achieving a deeper understanding of the behavior of machine learning algorithms.
Below is the 3 step process that you can use to get up-to-speed with statistical methods for machine learning, fast.
You can see all of the statistical methods posts here.Below is a selection of some of the most popular tutorials.
Understand Machine Learning Algorithms
Machine learning is about machine learning algorithms.
You need to know what algorithms are available for a given problem, how they work, and how to get the most out of them.
Heres how to get started withmachine learning algorithms:
You can see all machine learning algorithm posts here. Below is a selection of some of the most popular tutorials.
Weka Machine Learning (no code)
Weka is a platform that you can use to get started in applied machine learning.
It has a graphical user interface meaning that no programming is required and it offers a suite of state of the art algorithms.
Heres how you can get started with Weka:
You can see all Weka machine learning posts here. Below is a selection of some of the most popular tutorials.
Python Machine Learning (scikit-learn)
Python is one of the fastest growing platforms for applied machine learning.
You can use the same tools like pandas andscikit-learn in the development and operational deployment of your model.
Below are the steps that you can use to get started with Python machine learning:
You can see all Python machine learning posts here. Below is a selection of some of the most popular tutorials.
R Machine Learning (caret)
R is a platform for statistical computing and is the most popular platform among professional data scientists.
Its popular because of the large number oftechniques available, and because of excellent interfaces to these methods such as the powerful caret package.
Heres how to get started with R machine learning:
You can see all R machine learning posts here. Below is a selection of some of the most popular tutorials.
Code Algorithm from Scratch (Python)
You can learn a lot about machine learning algorithms by coding them from scratch.
Learning via coding is the preferred learning style for many developers and engineers.
Heres how to get started with machine learning by coding everything from scratch.
You can see all of the Code Algorithms from Scratch posts here.Below is a selection of some of the most popular tutorials.
Introduction to Time Series Forecasting (Python)
Time series forecasting is an important topic in business applications.
Many datasets contain a time component, but the topic of time series is rarely covered in much depth from a machine learning perspective.
Heres how to get started with Time Series Forecasting:
You can see all Time Series Forecasting posts here. Below is a selection of some of the most popular tutorials.
XGBoost in Python (Stochastic Gradient Boosting)
XGBoost is a highly optimized implementation ofgradient boosted decision trees.
It is popularbecause it is being usedby some of the best data scientists in the world to win machine learning competitions.
Heres how to get started with XGBoost:
You can see all XGBoosts posts here. Below is a selection of some of the most popular tutorials.
Deep Learning (Keras)
Deep learning is afascinating and powerful field.
State-of-the-art results are coming from the field of deep learning and it is asub-field of machine learning that cannot be ignored.
Heres how to get started with deep learning:
You can see all deep learning posts here. Below is a selection of some of the most popular tutorials.
Better Deep Learning
Although it is easy to define and fit a deep learning neural network model, it can be challenging to get good performance on a specific predictive modeling problem.
There are standard techniques that you can use to improve the learning, reduce overfitting, and make better predictions with your deep learning model.
Heres how to get started with getting better deep learning performance:
You can see all better deep learning posts here. Below is a selection of some of the most popular tutorials.
Long Short-Term Memory (LSTM)
Long Short-Term Memory (LSTM) Recurrent Neural Networks are designed for sequence prediction problems and are astate-of-the-art deep learning technique for challenging prediction problems.
Heres how to get started with LSTMs in Python:
You can see all LSTMposts here. Below is a selection of some of the most popular tutorials using LSTMs in Python with the Keras deep learning library.
Deep Learning for Natural Language Processing (NLP)
Working with text data is hard because of the messy nature of natural language.
Text is not solved but to get state-of-the-art results on challenging NLP problems, you need to adopt deep learning methods
Heres how to get started with deep learning for natural language processing:
You can see all deep learning for NLP posts here. Below is a selection of some of the most popular tutorials.
Deep Learning for Computer Vision
Working with image data is hard because of the gulf between raw pixels and the meaning in the images.
Computer vision is not solved, but to get state-of-the-art results on challenging computer vision tasks like object detection and face recognition, you need deep learning methods.
Heres how to get started with deep learning for computer vision:
You can see all deep learning for Computer Vision posts here. Below is a selection of some of the most popular tutorials.
Deep Learning for Time Series Forecasting
Deep learning neural networks are able to automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs.
Methods such as MLPs, CNNs, and LSTMs offer a lot of promise for time series forecasting.
Heres how to get started with deep learning for time series forecasting:
You can see all deep learning for time series forecasting posts here.Below is a selection of some of the most popular tutorials.
Generative Adversarial Networks
Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods, such as convolutional neural networks.
GANs are an exciting and rapidly changing field, delivering on the promise of generative models in their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks.
Heres how to get started with deep learning for Generative Adversarial Networks:
You can see all Generative Adversarial Networktutorials listed here. Below is a selection of some of the most popular tutorials.
Need More Help?
Im here to help you become awesome at applied machine learning.
If youstill have questions and need help, you have some options:
Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Use supervised learning if you have known data for the output you are trying to predict.
Supervised learning uses classification and regression techniques to develop predictive models.
Classification techniques predict discrete responsesfor example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, speech recognition, and credit scoring.
Use classification if your data can be tagged, categorized, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers. In image processing and computer vision, unsupervised pattern recognition techniques are used for object detection and image segmentation.
Common algorithms for performing classification include support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Nave Bayes, discriminant analysis, logistic regression, and neural networks.
Regression techniques predict continuous responsesfor example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.
Use regression techniques if you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment.
Common regression algorithms include linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.
Continue reading here: