This is a cheatsheet for comparing top ten related machine learning concepts. The goal of the cheatsheet is to explain ML concepts to a layman/intermediate, hiring manager and a programmer. Starting off with top ten ML models.
Prepared using info from several resources including documentation, this book and AI lectures on MIT OpenCourseWare.
- With the SKlearn code I simply write the code that values can be plugged into to return the classification or regression results
- On the from scratch example, I implement by writing my own functions and classes using the minimum possible amount of python libraries (in most cases numpy).
| Algorithm Explanation | Sklearn Code | From Scratch Example |
|---|---|---|
| 1. Linear Regression | Sklearn Code | Value |
| 2. Logistic Regression | Sklearn Code | LogisticRegression |
| 3. Naive Bayes | Sklearn Code | NB_Scratch |
| 4. K- Nearest Neighbors (KNN) | Sklearn Code | KNN_Scratch |
| 5. Decison Trees | Sklearn Code | DecisionTree |
| 6. Random Forest | Sklearn Code | Value |
| 7. XGBoost | Sklearn Code | Value |
| 8. Support Vector Machine (SVM) | Sklearn Code | SVM_scratch |
| 9. Principal Component Analysis | Sklearn Code | value |
| 10. K-means Clustering | Sklearn Code | Value |