How to Choose the Right Machine Learning Algorithm for Your Data Science Task

Best practices and useful guidelines to apply machine learning techniques for your problems

There are many machine learning and deep learning algorithms available today. You do not need to know their working principles to apply them to your data-related tasks. However, you need to know when to use which algorithm.

The first thing you should consider is that find out you actually need to apply machine learning or deep learning to solve your problem. Some problems can easily be solved without using machine learning or deep learning. If you apply machine learning techniques to those problems, they may become even more complicated.

If your problem is more likely to be data-driven, it is better to consider applying machine learning and deep learning techniques. Some complex problems like text classification, image classification, speech recognition, translations cannot be solved without using deep learning.

Here are some of the best practices and useful guidelines to follow when you apply machine learning and deep learning to your problems.

In machine learning, there are two main types of problems: Supervised and Unsupervised. Because of this, machine learning algorithms are also divided into these two classes. The supervised problems are further divided into Classification and Regression problems.

Linear regression is the most fundamental type of regression algorithm. On the other hand, logistic regression is used for classification tasks. Tree-based algorithms like decision trees, random forests, XGBoost, lCatBoost can be used for both classification and regression tasks.

The unsupervised problems are further divided into Clustering and Dimensionality Reduction. The K-Means algorithm is the most popular clustering algorithm while the Principal Component Analysis (PCA) is the most popular algorithm for dimensionality reduction.

Time series data needs special algorithms. Time series data consider the time element. They are a list of data points collected at sequential time points. The…

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