Introduction to Support Vector Machines in Machine Learning
If you&#x27;re new to ML or datascience, I would recommend working to build a strong basis in Bayesian statistics. It will help you understand how all of the &quot;canonical&quot; ML methods relate to one another, and will give you a basis for building off of them.<p>In particular, aspire to learn probabilistic graphical models + the libraries to train them (like pyro, tensorflow probability, Edward, Stan). They have a steep learning curve, especially if you&#x27;re new to the game, but the reward is great.<p>All of these methods have their place. SVM&#x27;s have their place, but also aren&#x27;t great for probability calibration and non-linear SVM&#x27;s like every single kernel method can scale absolutely terribly. Neural networks have their place, sometimes as a component of a larger statistical model, sometimes as a feature selector, sometimes in and of themselves. They&#x27;re also very often the wrong choice for a problem.<p>Don&#x27;t fall into the beginner trap: sometimes people tend to mistake &#x27;what is the hottest research topic&#x27; for &#x27;what is the right solution to my problem given my constraints, (data limitations, time limitations, skill limitations, etc.)&#x27;. Be realistic, don&#x27;t use magical thinking, and have a strong basis in statistics to weed out the beautiful non-bullshit from the bullshit that is frustratingly prevalent (everyone and their mother is an ML expert today).<p>EDIT: I want to also clarify: I don&#x27;t mean to suggest the author is new to ML, I just mean this as general advice for anyone coming here who is new to DS&#x2F;ML. The article looks great!