The choice of machine learning algorithm or technique that is best suited to a problem depends on several factors such as the nature of the data, the size of the dataset, the type of problem (classification, regression, clustering, etc.), and the desired level of accuracy.

Here are some commonly used machine learning algorithms and techniques and the types of problems they are suited for:

1. Linear Regression: Used for regression problems where the target variable is continuous and the input features are also continuous.

2. Logistic Regression: Used for classification problems where the target variable is binary (yes or no) or multi-class (more than two categories).

3. Decision Trees: Used for classification and regression problems where the data is structured and can be represented as a tree-like structure.

4. Random Forest: Used for classification and regression problems where the data is structured and consists of multiple decision trees.

5. Support Vector Machines (SVMs): Used for classification and regression problems where the data is linearly separable or can be separated into classes by a hyperplane.

6. Naive Bayes: Used for classification problems where the input features are categorical or discrete.

7. K-Nearest Neighbors (KNN): Used for classification and regression problems where the data is structured and the output variable is dependent on the neighboring data points.

8. Neural Networks: Used for a wide range of problems, including image classification, natural language processing, and speech recognition.

9. Clustering: Used for unsupervised learning problems where the goal is to group similar data points together.

The choice of the best algorithm or technique for a specific problem depends on a variety of factors such as the type of data, the size of the dataset, and the desired level of accuracy. It's important to experiment with different techniques to find the one that works best for your particular problem.