As a beginner you have chosen a multi-layer problem, each of which would be a good beginner problem.
I’ve found it useful to think of ML in 3 broad categories, classifiers, recommenders, and regressions. Event NLP is a classifier, taking some words and classifying each. As Gustav mentioned there are several problems here and in come cases multiple ways to approach them. So some questions: 1) does your experiment require natural language input? 2) do you want to search for content that is similar to the input or do you want a recommendation 3) probably others The most simple approach would be a search engine, which would take the natural language input and find the object with the most words in common. This would probably not be the best you could do but would be an easy first step. There is a great deal to know about how search engines work since they are basically a top k sum-of-dot-products similarity engine. This particular technique is called cosine similarity and is used in many ML aglos. For this PIO is not needed though you could use it if you wanted to also learn the framework since PIO includes Elasticsearch. On Dec 22, 2016, at 10:38 PM, Seshachalam M <[email protected]> wrote: Hi All, I scraped lot of data from a food delivery site(restaurants, menu, prices, user reviews and ratings). I want to train an ML with this data and able to ask questions like "where can i get best hot chocolate near me ?" "need biryani for 2 people and a salad, show me restaurants" It should respond after considering the user reviews/prices/ratings. I have gone through the prediction io docs but I dont have clear idea on what kind of engines to use to train with this data. Please guide me on how to proceed with this. Thanks, Sesha
