Machine Learning (ML) Neural Network (NN) Deep Learning (DL) Data Science My current understanding is that each of these encapsulates some set of algorithms. I feel like, amongst ML, NN, and DL, Machine Learning (ML) is the largest set, which contains NN, which in turn contains DL. See figure below. Data Science is the largest set amongst all 5.
14 Many people seem to agree that Arthur Samuel wrote or said in 1959 that machine learning is the " Field of study that gives computers the ability to learn without being explicitly programmed ". For example the quote is contained in this page, that one and in Andrew Ng's ML course.
In machine learning, the term "ground truth" refers to the accuracy of the training set's classification for supervised learning techniques. This is used in statistical models to prove or disprove research hypotheses. The term "ground truthing" refers to the process of gathering the proper objective (provable) data for this test.
I am trying to learn Machine Learning concepts these days. I understand in a traditional ML data, we will have features and labels. I have following toy data in my mind where I have features like '
I also have a column that shows which loan has been selected at the end by the user per each search. I am looking to find out which features of the loan were most important to users, i.e. try to predict what loan the user will select depending on his/her inputs. What ML model could I use for this? I am unsure how to approach the problem.
Do we train the model in three different environments? (IMO, No, but looking for justifications and understanding industry best practices) How do the dev, staging, and production environment work for model training in real-world scenarios? Note: ML models can be rule-based, recommendation models, or traditional ML/deep learning models.
I'm exploring a machine learning research direction and I'm looking for ideas or pointers to existing models/projects that fit the following setup: The model is trained on graphs with edge information (e.g., node features + edges).
Last 4-6 weeks, I have been learning and working for the first time on ML. Reading blogs, articles, documentations, etc. and practising. Have asked lot of questions here on Stack Overflow as well.
