[Cheat Sheet] I summarized the 10 most common ML Algorithms for my interview prep. Thought I'd share.

Hi everyone, I’ve been reviewing the basics for upcoming interviews, and I realized I often get stuck trying to explain simple concepts without using jargon. I wrote down a summary for the top 10 algorithms to help me memorize them. I figured this might help others here who are just starting out or refreshing their memory. Here is the list: # 1. Linear Regression * **The Gist:** Drawing the straightest possible line through a scatter plot of data points to predict a value (like predicting house prices based on size). * **Key Concept:** Minimizing the "error" (distance) between the line and the actual data points. # 2. Logistic Regression * **The Gist:** Despite the name, it's for **classification**, not regression. It fits an "S" shaped curve (Sigmoid) to the data to separate it into two groups (e.g., "Spam" vs. "Not Spam"). * **Key Concept:** It outputs a probability between 0 and 1. # 3. K-Nearest Neighbors (KNN) * **The Gist:** The "peer pressure" algorithm. If you want to know what a new data point is, you look at its 'K' nearest neighbors. If most of them are Blue, the new point is probably Blue. * **Key Concept:** It doesn't actually "learn" a model; it just memorizes the data (Lazy Learner). # 4. Support Vector Machine (SVM) * **The Gist:** Imagine two groups of data on the floor. SVM tries to put a wide street (hyperplane) between them. The goal is to make the street as wide as possible without touching any data points. * **Key Concept:** The "Kernel Trick" allows it to separate data that isn't easily separable by a straight line by projecting it into higher dimensions. # 5. Decision Trees * **The Gist:** A flowchart of questions. "Is it raining?" -> Yes -> "Is it windy?" -> No -> "Play Tennis." It splits data into smaller and smaller chunks based on simple rules. * **Key Concept:** Easy to interpret, but prone to "overfitting" (memorizing the data too perfectly). # 6. Random Forest * **The Gist:** A democracy of Decision Trees. You build 100 different trees and let them vote on the answer. The majority wins. * **Key Concept:** Reduces the risk of errors that a single tree might make (Ensemble Learning). # 7. K-Means Clustering * **The Gist:** You have a messy pile of unlabelled data. You want to organize it into 'K' number of piles. The algorithm randomly picks centers for the piles and keeps moving them until the groups make sense. * **Key Concept:** Unsupervised learning (we don't know the answers beforehand). # 8. Naive Bayes * **The Gist:** A probabilistic classifier based on Bayes' Theorem. It assumes that all features are independent (which is "naive" because in real life, things are usually related). * **Key Concept:** Surprisingly good for text classification (like filtering emails). # 9. Principal Component Analysis (PCA) * **The Gist:** Data compression. You have a dataset with 50 columns (features), but you only want the 2 or 3 that matter most. PCA combines variables to reduce complexity while keeping the important information. * **Key Concept:** Dimensionality Reduction. # 10. Gradient Boosting (XGBoost/LightGBM) * **The Gist:** Similar to Random Forest, but instead of building trees at the same time, it builds them one by one. Each new tree tries to fix the mistakes of the previous tree. * **Key Concept:** Often the winner of Kaggle competitions for tabular data. Let me know if I missed any major ones or if you have a better analogy for them!