In the current technological field, machine learning is an important component that is in the race towards artificial intelligence. Whenever you are trying to gain insight information from all the data which you have been collecting, machine learning is a significant step to learn forward. So, machine learning is the way for computers for running many algorithms without direct human oversights in order to learn from data. Also, machine learning can include running any variety of tasks in order for the machine to determine a high-probability outcome for various information such as the functions between input and output structures. Below are the machine learning algorithms for beginners.

Linear Regression

Simple linear regression model - 365 Data Science

Predictive modeling is mainly concerned with minimizing the error of the algorithm or having the most accurate predictions practicable, at the cost of the ability to explain. We can borrow, reuse, and steal algorithms from a number of areas, like statistics, and use them for these purposes. Different techniques can be used for learning the linear regression model from data, such as a linear algebra solution for ordinary least squares and gradient descent optimization.

Logistic Regression

Logistic Regression is another technique that is borrowed by machine learning from the field of statistics. It can be the go-to method for binary classification problems. Logistic regression is like linear regression in that the goal is to find the values for the coefficients which can weight each input variable. Unlike linear regression, the output estimate is converted using a non-linear function called a logistic function. So, logistic regression is a fast model to learn and effective on binary classification problems.

Linear Discriminant Analysis

Logistic Regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes the linear discriminant analysis algorithm is the preferred linear classification technique. Also, the representation of LDA is straight forward and consists of statistical properties of your data, calculated for each class.

Classification And Regression Trees

Cost-Sensitive Decision Trees for Imbalanced Classification

Decision Trees are an important type of algorithm for predictive modeling machine learning. The representation of the decision tree model is a binary tree.  So, decision trees are very fast to learn for making predictions and also give accurate results for a broad range of problems and do not require any special preparation of your data. This method is useful in machine learning algorithms for beginners.

Naive Bayes

Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. The model can be comprised of two types of probabilities which can be calculated directly from your training data. So, once it is calculated, the probability model can be used for making predictions for new data using Bayes Theorem.

Using these five machine learning algorithms may not be difficult, but it takes time for them to learn. Hope that I have covered all the topics in my article about machine learning algorithms for beginners. Thanks for reading!