Series
AI Engineering
Machine learning and deep learning notes. 26 posts.
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How a support vector machine chooses a decision boundary by maximizing its margin
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How classification models measure probability errors and learn from them
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The third weekly paper on decision trees, major boosting models, and the difference between PCA and factor analysis
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Why linear regression is unsuitable for classification and how logistic regression separates classes with probabilities
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A Codeit mission on reading feature context, outliers, and model behavior in Kaggle's Bike Sharing Demand data
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How feature scale affects model training and why Ridge, Lasso, and Elastic Net depend on comparable inputs
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The second weekly paper on how machine-learning models learn, fail to generalize, and are evaluated
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The main ways machine-learning systems learn from data and how they differ
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How vectors, matrices, and linear systems connect to machine-learning inputs and weight calculations
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The first weekly paper on the mathematical language of machine learning and the preparation of data for analysis
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Reviewing how descriptive statistics and visualizations explain center, spread, and distribution shape
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A lesson note on filtering data, creating values, and aggregating groups with Pandas
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A first pass through selecting, combining, and cleaning tabular data with Pandas
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A lesson note on vectors, matrices, NumPy arrays, shape, axis, and basic array operations
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Notes on Python sequences, file I/O, duplicate removal using sets, and OOP fundamentals covered in the second class of Codeit Sprint Main Course.
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A first-class Codeit Sprint note on data science, MLOps, and Python basics.
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A learning note on how hypothesis functions, loss functions, partial derivatives, and gradient vectors lead to gradient descent.
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A learning note on linear regression, mean squared error, and why the loss function takes theta as input.
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A learning note on partial derivatives and gradients from a machine learning fundamentals course
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A Codeit AI Engineer Sprint note on reviewing functions, rates of change, and the basic idea behind derivatives
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A summary of matrix addition, scalar multiplication, matrix multiplication, element-wise multiplication, and NumPy operations
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Reviewing the fundamentals of machine learning and introductory linear algebra in the Codeit pre-course.
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A Codeit AI Engineer Sprint pre-course note on dictionary view objects, string immutability, and Python indexing
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A short Codeit AI Engineer Sprint pre-course note on revisiting Python lists and for loops
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A short note from the Codeit AI Engineer Sprint pre-course on reviewing Python basics, PEP 8, and scope
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Returning to a structured learning routine to keep growing as a hands-on engineer in the AI era