@datascibykashi: Machine Learning can look complicated at first. But the fundamentals are surprisingly structured. Hereโs your quick reference. ๐ 1๏ธโฃ TYPES OF MACHINE LEARNING ๐ต Supervised Learning Learn from labeled data. Examples: ๐ Regression ๐ฏ Classification ๐ฃ Unsupervised Learning Find patterns in unlabeled data. Examples: ๐ Clustering ๐ Dimensionality Reduction ๐ข Reinforcement Learning Learn through rewards and penalties. 2๏ธโฃ COMMON ALGORITHMS ๐ REGRESSION ๐น Linear Regression ๐น Ridge / Lasso ๐น Decision Tree Regression ๐น Random Forest Regression ๐น Gradient Boosting ๐ฏ CLASSIFICATION ๐น Logistic Regression ๐น KNN ๐น Decision Tree ๐น Random Forest ๐น SVM ๐น Gradient Boosting ๐ CLUSTERING ๐น K-Means ๐น DBSCAN ๐น Hierarchical Clustering 3๏ธโฃ BASIC ML WORKFLOW ๐ ๐ฅ Collect Data โฌ๏ธ ๐งน Clean Data โฌ๏ธ ๐ EDA โฌ๏ธ โ๏ธ Feature Engineering โฌ๏ธ โ๏ธ Train / Validation / Test Split โฌ๏ธ ๐ค Train Model โฌ๏ธ ๐ Evaluate โฌ๏ธ ๐ฏ Tune โฌ๏ธ ๐ Deploy โฌ๏ธ ๐ Monitor 4๏ธโฃ OVERFITTING VS UNDERFITTING ๐ด Overfitting Model learns the training data too well. โก๏ธ Great training performance โก๏ธ Poor unseen-data performance ๐ก Underfitting Model is too simple to capture important patterns. โก๏ธ Poor training performance โก๏ธ Poor test performance ๐ข Good Fit Learns useful patterns and generalizes well. 5๏ธโฃ BIAS VS VARIANCE ๐ High Bias โ Model too simple ๐ High Variance โ Model too sensitive to training data ๐ฏ Goal โ Find a balance. 6๏ธโฃ CLASSIFICATION METRICS ๐ฏ Accuracy Correct predictions / Total predictions ๐ Precision Of predicted positives, how many were actually positive? ๐ก Recall Of actual positives, how many did we identify? โ๏ธ F1 Score Balances precision and recall. ๐ ROC-AUC Measures ranking/discrimination ability across classification thresholds. 7๏ธโฃ REGRESSION METRICS ๐ MAE Average absolute error. ๐ MSE Average squared error. โ RMSE Square root of MSE. ๐ Rยฒ Measures how much variance in the target is explained by the model. 8๏ธโฃ CROSS-VALIDATION ๐ Instead of relying on one train/test split, divide the training data into multiple folds. Train on some folds โ Validate on another โ Repeat. Useful for: โ More reliable model evaluation โ Model comparison โ Hyperparameter tuning 9๏ธโฃ FEATURE ENGINEERING โ๏ธ Transform raw variables into useful features. Examples: ๐ Extract month from a date ๐ข Create ratios ๐ท๏ธ Encode categories ๐ Scale numerical features ๐งฉ Create interaction features ๐ REGULARIZATION ๐ก๏ธ Helps reduce overfitting. L1 โ Lasso L2 โ Ridge Regularization adds a penalty that discourages overly complex model parameters. ๐ง QUICK ALGORITHM GUIDE ๐ Predict a number โ Regression ๐ฏ Predict a class โ Classification ๐ฅ Find groups โ Clustering ๐ Reduce dimensions โ PCA ๐ณ Strong tabular baseline โ Random Forest / Gradient Boosting ๐ผ๏ธ Images โ CNN / Vision models ๐ฌ Language โ Transformers ๐ THE GOLDEN RULE Donโt memorize algorithms. Understand: โ What problem does it solve? โ What assumptions does it make? โ When should I use it? โ How do I evaluate it? โ How does it fail? Thatโs what turns ML knowledge into practical ML skills. ๐ฅ ๐ Save this cheat sheet for your next Machine Learning project. #MachineLearning #ML #DataScience #creatorsearchinsights #machinelearningengineer
