Machine Learning Algorithms

The prevalence of machine learning has been increasing tremendously in recent years due to the high demand and advancements in technology. The potential of machine learning to create value out of data has made it appealing for businesses in many different industries. Most machine learning products are designed and implemented with off-the-shelf machine learning algorithms with some tuning and minor changes.

There is a wide variety of machine learning algorithms that can be grouped in three main categories:

Supervised learning algorithms

In this algorithms model the relationship between features (independent variables) and a label (target) given a set of observations. Then the model is used to predict the label of new observations using the features. Depending on the characteristics of target variable, it can be a classification (discrete target variable) or a regression (continuous target variable) task.

Unsupervised learning algorithms

They try to find the structure in unlabeled data.

Reinforcement learning algorithms

They works based on an action-reward principle. An agent learns to reach a goal by iteratively calculating the reward of its actions.

In this repository, I will cover the most common algorithms in the all categories. In further notes types are explained with definition.

Broadly there are 3-types of Machine Learning Algorithms

1. Supervised Learning

How it works: This algorithm consist 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 a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

2. Unsupervised Learning

How it works: 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.

3. Reinforcement Learning:

How it works: 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

List of Common Machine Learning Algorithms

List of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:

1. Linear Regression

2. Logistic Regression

3. Decision Tree

4. SVM

5. Naive Bayes

6. kNN

7. K-Means

8. Random Forest

9. Dimensionality Reduction Algorithms

10. Gradient Boosting algorithms

    a. GBM
    
    b. XGBoost
    
    c. LightGBM
    
    d. CatBoost
    

Regression Models

1. Linear Regression
2. Logistic Regression

Classification Models

1. Decision Tree
2. Random Forest

Clustering Models

1. K Means Clustering
2. Hirarchical Clustering
3. Spectral Clustering