machine learning algorithm cheat sheet

The Azure Machine Learning Algorithm Cheat Sheet helps you choose the right algorithm from the designer for a predictive analytics model.

Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression, and text analytics families. Each is designed to address a different type of machine learning problem.

Download: Machine Learning Algorithm Cheat Sheet

Start in the large blue box, ā€œWhat do you want to do?ā€ Then follow the lines out to match what you would like to solve. For example, maybe you have some data and you want to predict whether a customer will purchase or not. You want to predict ā€œWill Purchaseā€ or ā€œWill Not Purchaseā€. Thus you are trying to predict between two categories. Here is how you work through the diagram.

  1. Start at ā€œWhat do you want to do?ā€
  2. Follow the thin blue line labeled ā€œPredict between two categoriesā€
  3. Arrive at the Two-Class Classification box
  4. Choose from the algorithms in the box

Helpful, donā€™t you think?

Top 8 Machine Learning algorithms that are shaping the future of AI

machine learning algorithms

1ļøāƒ£ Linear Regression: A fundamental algorithm for regression tasks, modeling relationships between variables.

2ļøāƒ£ Logistic Regression: Key for binary classification problems, predicting outcomes between two classes.

3ļøāƒ£ Decision Trees: Versatile, interpretable, and efficient for both classification and regression tasks.

4ļøāƒ£ Random Forest: An ensemble of decision trees, robust for various tasks due to its high accuracy and resilience to overfitting.

5ļøāƒ£ Support Vector Machines (SVM): Great for classification, separating data points in multidimensional space.

6ļøāƒ£ K-Nearest Neighbors (KNN): Simple yet effective for both classification and regression by using nearby data points.

7ļøāƒ£ Clustering Algorithms (e.g., K-means): Essential for unsupervised learning, grouping data into clusters based on similarities.

8ļøāƒ£ Neural Networks (Deep Learning): Complex models mimicking the human brain, excelling in intricate tasks like image and speech recognition.

Understanding these algorithms is crucial for anyone diving into the ML world! šŸŒ Let’s keep pushing boundaries together! šŸ’”