I like to think of ML tasks as questions in a certain format, for which the system we're building gives answers. The question has to be about a certain "object" of the real world (which we call the input). In the supervised learning paradigm -- which we're focusing on in this series -- we would make the system learn from example objects AND from the answers for each of them. The inputs in those questions are an email and a property. The Data Sources listed in the LEARN part of the Canvas (see Part II) should provide information about these inputs.
"But what we're seeing is that machine learning is changing this whole game. Machine learning is basically a game-changer because now it can leverage these low cost cameras and of course now we can automate a process like that and you're not paying for expensive lab tests, you're not paying for expensive people. The camera can work 24/7 . . . This goes across every industry. I got a call the other day from somebody who wants to inspect glass - windows and doors. This just goes across everywhere people have some sort of quality control process, quality assurance, and they just want to know what's going on."
Native scoring is a much overlooked feature in SQL Server 2017 (available only under Windows and only on-prem), that provides scoring and predicting in pre-build and stored machine learning models in near real-time. Depending on the definition of real-time, and what does it mean for your line of business, I will not go into the definition of real-time, but for sure, we can say scoring 10.000 rows in a second from a mediocre client computer (similar to mine) . Native scoring in SQL Server 2017 comes with couple of limitations, but also with a lot of benefits. Overall, if you are looking for a faster predictions in your enterprise and would love to have a faster code and solution deployment, especially integration with other applications or building API in your ecosystem, native scoring with PREDICT function will surely be advantage to you. Although not all of the predictions/scores are supported, majority of predictions can be done using regression models or decision trees models (it is estimated that both type (with derivatives of regression models and ensemble methods) of algorithms are used in 85% of the predictive analytics).
Does anyone here have experience with using ML models to predict markets? I've found it very challenging so far, and I need help. This is how far I've gotten: Plots at the top, in light green background, are predictions using training data. Plots at the bottom, in light blue background, are predictions using testing data. Blue lines are historical prices of a stock/cryptocurrency. Red lines are predicted future 5 minute prices, made at time at which the blue line ends.
In this work we study the problem of using machine-learned predictions to improve performance of online algorithms. We consider two classical problems, ski rental and non-clairvoyant job scheduling, and obtain new online algorithms that use predictions to make their decisions. These algorithms are oblivious to the performance of the predictor, improve with better predictions, but do not degrade much if the predictions are poor. Papers published at the Neural Information Processing Systems Conference.