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There are space and time considerations for each machine learning algorithm. Third, and perhaps most important, is the output that you want to get.
 Evaluation Metrics for Unsupervised Learning Algorithms
Are you trying to categorize data? Use it to predict future data points? Your best bet here is probably linear regression, something that can take a whole host of factors and then give you a predictive result with a simple error rate explanation and a simple explanation for which factors contribute to the prediction.
Resource : Linear Regression — Detailed View. The first instinct you should have when you see a situation like this is to apply the logistic regression model. Resource : Building a Logistic Regression. The first natural fit for this problem is the K-Means clustering algorithm, which will group and cluster data by measuring the distance between each point.
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Your best bet here is probably Naive Bayes, which is a simple but powerful model that can be used for text classification. Another decent bet is logistic regression, which is a simple model to grasp and explain, and less hard to pick apart than Naive Bayes which will often assign probabilities word by word rather than holistically labeling a text snippet as being part of one group or another. Moving on to something more powerful, a Linear Support Vector Machine algorithm will likely help improve your performance. Lastly, bag-of-words analysis could also work — consider doing an ensemble of different methods and testing all of these methods against one another, depending on the dataset in question.
The best algorithm to tackle going through different images is a convolutional neural network that is organized similarly to how animal visual cortexes are analyzed.
Measured by performance reduced error rate in the ImageNet competition, the SE-Resnet architecture comes out on top, though as the field is still developing, new advances come out almost every day. You should be aware, however, that convolutional neural networks are dense and require a lot of computational power — so make sure that you have the hardware capability to run these models on large-scale datasets.
The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots perception, control , text understanding web search, anti-spam , computer vision, medical informatics, audio, database mining, and other areas. It is the best online course for any person wanna learn machine learning.
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Andrew sir teaches very well. His pace is very good. The insights which you will get in this course turns out to be wonderful. Sub title should be corrected. Since I'm not that good in English but I know when there're mis-traslated or wrong sub title. If you fix this problems , I thin it helps many students a lot. Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.
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Top Machine Learning Algorithms You Should Know to Become a Data Scientist
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