How can I find anomalies in each row of data? I have some reported data I want to spot anomalies on. The columns are a facility name then monthly reports of that given facility.

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| Facility  | 2017  Jan Visitors | 2017 Feb Visitors | 2017  March Visitors |
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| Facility 1|         1234       |       1345        |  100345              |
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| Facility 2|        56          |      567          | 34                   |
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How can train this panda dataframe like this row-wise?

Planning to use one class SVM from sklearn. I want to get the anomalies in each facility e.g. in Facility 1 I'd mark 100345 as an anomaly. I have data spanning a couple of years. While we are here I am a super noob in ML and data science can I get a pointer to a condensed tutorial on unsupervised learning most of the ones I am coming across are for supervised.
I am assuming your goal is a time series anomaly detection, in which you want to detect the occurrence of outlier as time goes by. To achieve that, you need to set up a time series prediction model first, and then perform outlier detection base on that. 

For unsupervised outlier detection, just check Andrew Ng's machine learning course on Coursera, there is a chapter about anomaly detection based on gaussian kernel. You can also read sklearn docments for OneClassSVM, LocalOutlierFactor, etc. Basically, the core idea of unsupervised anomaly detection is setup a metrics to describe the affinity between samples, and then use a user defined threshold to decide if a new sample is similar enough to other samples.