SVM and Random Forest are both used in classification problems.
a)If you are sure that your data is outlier free and clean then go for SVM. Itis the opposite -if your data might contain outliers then Random forest wouldbe the best choice
b)Generally, SVM consumes more computational power than RandomForest, so if you are constrained with memory go for Random Forestmachinelearning algorithm.
c)Random Forest gives you a very good idea of variable importance in yourdata, so if you want to have variable importance then choose Random Forestmachine learning algorithm.
d)Random Forest machine learning algorithms are preferred for multiclassproblems.
e)SVM is preferred in multi-dimensional problem set - like text classificationbut as a good data scientist, you should experiment with both of them and test for accuracy or rather you can use ensemble of many MachineLearning techniques.