Broadly, there are three kinds of machine learning algorithms.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves the desired level of accuracy on the training data.
Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention.
Examples of Unsupervised Learning: Apriori algorithm, K-means.
Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
Example of Reinforcement Learning: Markov Decision Process.
This is a list of commonly used machine learning algorithms:
- Linear Regression
- Logistic Regression
- Decision Tree
- Naive Bayes
- Random Forest
- Dimensionality Reduction Algorithms
- Gradient Boosting algorithms: GBM, XGBoost, LightGBM, CatBoost
R Programming language has a package called ‘MLR’ which is absolutely incredible at performing machine learning tasks. This package includes all of the ML algorithms which we use frequently.
Examples of some analytics vidya projects/challenges where you can use R for Machine Learning:
- Predict Number of Upvotes
- Practice Problem: HR Analytics
- Food Demand Forecasting Challenge
- Loan Prediction III
- Recommendation Engine
- Black Friday Sales Prediction
Refer this link for more projects: Contests | Analytics Vidhya
Originally answered in Quora. Click to view post.
Pic Credits: Photo by Franki Chamaki