The Machine Learning Landscape
A broad overview of machine learning systems, tasks, and the workflow for turning data into useful models.
Notes.
Personal learning notes from reading data science and programming books.
Machine Learning
9 notes
End-to-End Machine Learning
Practical project workflow notes covering data preparation, validation, model training, and evaluation.
Classification
Classification concepts, metrics, decision thresholds, and ways to evaluate model performance.
Training Models
Core training ideas including regression, gradient descent, regularization, and optimization behavior.
Support Vector Machines
Support vector machines, margins, kernels, similarity features, and related optimization ideas.
Decision Trees
Decision tree intuition, splitting criteria, complexity, overfitting, and random forest foundations.
Ensemble Learning and Random Forests
Bagging, boosting, random forests, and ensemble methods for improving model performance.
Dimensionality Reduction
Dimensionality reduction, principal component analysis, manifold learning, and techniques for preserving useful structure in lower-dimensional spaces.
Unsupervised Learning Techniques
Clustering, anomaly detection, density estimation, K-means, DBSCAN, and other techniques for learning patterns from unlabeled data.
Programming
8 notes
Backtracking
Notes on backtracking from my learning vault.
Bit Operations
Notes on bit operations from my learning vault.
Dynamic Arrays
Notes on dynamic arrays from my learning vault.
Dynamic Programming
Notes on dynamic programming from my learning vault.
Greedy Algorithm
Notes on greedy algorithm from my learning vault.
Set Structures
Notes on set structures from my learning vault.
Sorting and Searching
Notes on sorting and searching from my learning vault.
Time Complexity
Notes on time complexity from my learning vault.