Machine Learning Basics Study Notes

Activation Functions

Activation functions inject non-linearity into neural networks, letting them approximate complex functions instead of collapsing into a single linear transform…

Anomaly Detection Basics

An overview of how machine learning identifies rare, unusual data points using statistical, distance-based, and model-based techniques across supervised and un…

Bias-Variance Tradeoff

A foundational concept explaining how model error decomposes into bias, variance, and irreducible noise, and how balancing them guides model complexity choices.

Common Machine Learning Pitfalls

A tour of the mistakes that most often derail machine learning projects, from data leakage to misleading metrics, and how to catch them before they cost you.

Confusion Matrix and Classification Metrics

A structured breakdown of correct and incorrect predictions by class, forming the foundation for accuracy, precision, recall, and other classification metrics.

Cross-Validation Strategies

Learn how k-fold, stratified, and time-series cross-validation give more reliable estimates of model performance than a single train-test split.

Data Cleaning Basics

Covers the core techniques for detecting and fixing messy real-world data — duplicates, inconsistent formatting, outliers, and type errors — before it reaches…

Datasets, Features, and Labels

Explains the vocabulary and structure of ML data — datasets, samples, features, and labels — and how they are organized before a model can be trained.

DBSCAN and Density-Based Clustering

Learn how DBSCAN groups points by local density rather than distance to a centroid, letting it discover arbitrarily shaped clusters and flag outliers automatic…

Decision Trees

Covers how decision trees split data recursively using impurity measures like Gini and entropy to produce interpretable, rule-based predictions.

Dimensionality Reduction and PCA

See how Principal Component Analysis compresses many correlated features into fewer uncorrelated components while preserving most of the variance in data.

Encoding Categorical Variables

Learn how to convert non-numeric categories into numeric representations models can use, and when to choose one-hot, ordinal, or target encoding.

Evaluating Regression Models

Surveys the core metrics for assessing regression quality — MAE, MSE, RMSE, and R-squared — and explains when each is the right lens for model performance.

Feature Scaling and Normalization

Learn why rescaling numeric features onto comparable ranges matters, and how standardization, min-max scaling, and robust scaling each affect model behavior.

Feature Selection Techniques

Discover filter, wrapper, and embedded methods for choosing the most useful subset of features, reducing overfitting and improving model interpretability.

Gradient Descent Explained

Understand how gradient descent iteratively adjusts model parameters to minimize a loss function, and how learning rate and variants like SGD affect convergenc…

Handling Missing Data

Explores why data goes missing, the different missingness mechanisms, and practical strategies — from deletion to imputation — for preparing incomplete dataset…

Hierarchical Clustering

An unsupervised technique that builds a nested tree of clusters (a dendrogram) by successively merging or splitting groups, avoiding the need to pre-specify a…

Hyperparameter Tuning

Explore systematic strategies — grid search, random search, and cross-validation-based tuning — for choosing model settings that are not learned directly from…

Intro to scikit-learn and the ML Toolchain

Scikit-learn provides a consistent fit/predict interface for building, evaluating, and chaining machine learning models, and anchors the wider Python ML toolch…

k-Means Clustering

An unsupervised algorithm that partitions data into k groups by iteratively assigning points to the nearest centroid and recomputing centroids until convergenc…

k-Nearest Neighbors (k-NN)

Explains the instance-based k-NN algorithm, which classifies or predicts based on the majority vote or average of the k closest training examples.

Linear Regression Explained

Understand how linear regression fits a straight-line relationship between features and a continuous target, and how it is trained and evaluated.

Logistic Regression Explained

Introduces logistic regression as a classification algorithm that models class probability via the sigmoid function, despite its regression-sounding name.