Algorithms Data Scientists Machine Learning

 

The science of data science is constantly changing, but some machine learning algorithms are crucial for data scientists. Here's a breakdown of some of the most important algorithms to know by 2024, based on one or more changes: Good for understanding the relationship between features.

1. Logistic Regression: For categorical prediction, logistic regression analyzes the relationship between features to predict the probability of a particular event.
2. Decision trees: These algorithms use tree-like structures to classify data and make predictions. It is easy to explain and understand.
3. Random Forest: An integrated method that combines multiple decision trees to increase stability and accuracy.
4. Support Vector Machines (SVM): Powerful algorithms for classification, regression and even detection.
5. K Nearest Neighbors (KNN): This method predicts values ​​by finding the closest data (neighbors) in the data set.
6. Naive Bayes: A method of constructing a distribution that calculates the probability of an event based on independent features.
7. Unsupervised Learning:
8. K-Means Clustering: A simple, unsupervised method that groups unlabeled data points into a predetermined number of groups.
9. Other important algorithms:Gradient boosting: This integration technique combines poor learners (such as decision trees) back into a good model.
10. XGBoost: A powerful form of gradient boosting for real-time tasks such as text classification and sentiment analysis.

Beyond Algorithms:

While these algorithms are important at home, remember that data science is about more than choosing the right tools. Good data scientists understand the problem they are trying to solve, can evaluate the strengths and weaknesses of different approaches, and know how to interpret and communicate effectively.