codeit-ai-main-course

How a support vector machine chooses a decision boundary by maximizing its margin

How accuracy, recall, precision, and F1 expose different kinds of classification errors

How decision trees choose splits, why they overfit, and how voting, bagging, and boosting address their weaknesses

How classification models measure probability errors and learn from them

The third weekly paper on decision trees, major boosting models, and the difference between PCA and factor analysis

Why linear regression is unsuitable for classification and how logistic regression separates classes with probabilities

A Codeit mission on reading feature context, outliers, and model behavior in Kaggle's Bike Sharing Demand data

How feature scale affects model training and why Ridge, Lasso, and Elastic Net depend on comparable inputs

The second weekly paper on how machine-learning models learn, fail to generalize, and are evaluated

The main ways machine-learning systems learn from data and how they differ

How vectors, matrices, and linear systems connect to machine-learning inputs and weight calculations

The first weekly paper on the mathematical language of machine learning and the preparation of data for analysis

A lesson note on filtering data, creating values, and aggregating groups with Pandas

A first pass through selecting, combining, and cleaning tabular data with Pandas

A lesson note on vectors, matrices, NumPy arrays, shape, axis, and basic array operations

Notes on Python sequences, file I/O, duplicate removal using sets, and OOP fundamentals covered in the second class of Codeit Sprint Main Course.

A first-class Codeit Sprint note on data science, MLOps, and Python basics.