Machine Learning Cheatsheet

Introduction to Machine Learning

Machine learning (ML) is a subset of artificial intelligence that focuses on building systems that learn from data to improve their performance on a given task over time without being explicitly programmed. This cheatsheet aims to provide a quick reference guide for essential concepts, algorithms, and techniques in machine learning.

Key Concepts in Machine Learning

1. Supervised Learning

In supervised learning, models are trained on a labeled dataset, which means that each training example is paired with an output label. Common algorithms include:

• Linear Regression
• Logistic Regression
• Support Vector Machines (SVM)
• Decision Trees
• Random Forests
• Neural Networks

2. Unsupervised Learning

Unsupervised learning involves training a model on a dataset without labeled responses. The goal is to uncover hidden patterns or structures in the data. Common techniques include:

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)
• t-Distributed Stochastic Neighbor Embedding (t-SNE)

3. Reinforcement Learning

Reinforcement learning is an area of machine learning focused on how agents ought to take actions in an environment to maximize a cumulative reward. Key components include:

• Agent
• Environment
• Actions
• Rewards
• Policies

Common Machine Learning Algorithms

1. Linear Regression

Linear regression is used to predict the value of a continuous variable based on the value of one or more predictor variables. It assumes a linear relationship between inputs and outputs.

2. Decision Trees

Decision trees are flowchart-like structures that split data into branches to make predictions. They are easy to interpret and visualize.

3. Support Vector Machines (SVM)

SVMs are supervised learning models used for classification and regression analysis. They work by finding the hyperplane that best separates different classes.

Best Practices in Machine Learning

When working on machine learning projects, consider the following best practices:

• Understand the data: Conduct exploratory data analysis to grasp the dataset's characteristics.
• Feature engineering: Create new features that can improve model performance.
• Train-test split: Always split your data into training and testing sets to evaluate model performance.
• Model evaluation: Use appropriate metrics such as accuracy, precision, recall, and F1-score to assess model performance.
• Hyperparameter tuning: Optimize hyperparameters to improve model performance.

Useful Links