Machine Learning for Dummies
Hi Everyone, I chose to compose a post I’ve been wishing existed for quite a while. A basic presentation for the individuals who constantly needed to comprehend machine learning. Just true issues, down to earth arrangements, basic language, and no significant level hypotheses.
In a very layman manner, Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance.
Basic Difference in ML and Traditional Programming?
Traditional Programming : We feed in DATA (Input) + PROGRAM (logic), run it on machine and get output.
Machine Learning : We feed in DATA(Input) + Output, run it on machine during training and the machine creates its own program(logic), which can be evaluated while testing.
Classical machine learning is often divided into two categories –
Supervised and Unsupervised Learning.
Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is a learning in which we teach or train the machine using data which is well labeled that means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.
It has two parts:
1. Classification : Splits objects based at one of the attributes known beforehand. Separate socks by based on color, documents based on language, music by genre.
Popular algorithms: Naive Bayes, Decision Tree, Logistic Regression,
K-Nearest Neighbours.
2. Regression : Regression is basically classification where we forecast a number instead of category. Examples are car price by its mileage, traffic by time of the day, demand volume by growth of the company etc. Regression is perfect when something depends on time.
Popular algorithms: Linear Regression, Support Vector Machine.
Unsupervised Learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of machine is to group unsorted information according to similarities, patterns and differences without any prior training of data.
It has three parts:
1. Clustering: It is a classification with no predefined classes. It’s like dividing socks by color when you don’t remember all the colors you have. Divides objects based on unknown features. Machine chooses the best way.
Popular algorithms: K-means_clustering, Mean-Shift, DBSCAN.
2. Dimensionality Reduction: It is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
Popular algorithms: Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Latent Dirichlet allocation (LDA), Latent Semantic Analysis (LSA, pLSA, GLSA), t-SNE (for visualization).
Association Rule Learning: This includes all the methods to analyze shopping carts, automate marketing strategy, and other event-related tasks. When you have a sequence of something and want to find patterns in it — try these things. Say, a customer takes a six-pack of beers and goes to the checkout. Should we place peanuts on the way? How often do people buy them together?
