Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer.
Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers -- and more -- if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques.
This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.
- Introduction to machine learning: Five things the quants wish we knew – Are you trying to talk about machine learning with someone on a different technical level? This article helps bridge that chasm. Read it, and see if you find it easier to talk to data scientists about machine learning afterwards.
- One button to help them all – Drivers and automotive manufacturers both stand to benefit from connected cars, and the data streaming from them. How will machine learning play a role in analyzing this data? Read to find out.
- Machine learning is crucial for fraud management – Fraud is becoming more complex every day, and the amount of data needed to identify fraud continues to grow. Machine learning is the perfect technology to help address the problem in every industry.
- Can advanced analytics for credit scoring change the mortgage market? – Equifax found that machine learning does a better job of predicting creditworthiness than previous methods. As a result, participating lenders can now safely lend money to more people.
- Machine learning + wearable medical devices = a healthier future for all – When healthcare providers can collect and analyze data from wearable devices, we all become healthier, and in-home health care becomes easier.
- Machine Learning: An invited guest to the IoT party? – Research indicates that IoT and Machine Learning are more valuable to utilities when used in combination but there are hurdles to overcome first. Learn more in this summar of recent research.
- Machine learning changes the way we forecast in retail and CPG – Learn how machine learning-based forecasting can analyze millions of products using unlimited amounts of causal factors simultaneously up and down a company's business hierarchy.
- Sentiment analysis, machine learning open up world of possibilities – When recovering from a natural disaster, officials could use machine learning and sentiment analysis to visualize patterns between social media updates and existing field data to better allocate resources and sharpen future preparedness efforts. And that's just one use case mentioned here.
- Deep learning methods and applications – Deep learning is a type of machine learning that combines advances in computing power and special types of neural networks to learn complicated patterns in large amounts of data. Learn more about it in this interview.
- How intelligent machines learn to make sense of the world? – From supervised learning and unsupervised learning to reinforcement learning, understand the different types of machine learning and how they're used today.
- The difference between Statistical Modeling and Machine Learning, as I see it – For the Statistician the model comes first; for the Machine Learner the data are first, says SAS CTO Oliver Schabenberger. Read other comparisons in this post on LinkedIn.
From managers to data scientists and from retailers to health care professionals, machine learning is effecting us all. Read and share the articles here that matter most to your industry and your field, and let us know in the comments what other machine learning articles you've found useful too.
This content was reposted from the SAS Learning Post. Go there to view the original.