For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Co

For a beginner to machine learning I'd recommend Andrew Ng's course notes and lectures over any textbook I've seen. But I prefer his Stanford CS 229 notes to Coursera for exactly the reasons you state: they are watered down. After you really can understand Andrew Ng's course notes I'd recommend a textbook because they go in more detail and cover more topics. My two favorites for general statistical machine learning are: * Pattern Recognition and Machine Learning by Christopher M. Bishop * The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman Both are very intensive, perhaps to a fault. But they are good references and are good to at least skim through after you have baseline machine learning knowledge. At this stage you should be able to read almost any machine learning paper and actually understand it.