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Machine Learning
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insper/ML
Machine Learning
insper/ML
Overview
2026.2
2026.2
Overview
Classes
Classes
F1. Introduction & History
F2. The ML Landscape
D1. Exploratory Data Analysis
D2. Data Preprocessing
D3. Pipelines
U1. Dimensionality Reduction
U2. Clustering
R1. Linear Regression
R2. Gradient Descent & Regularization
C1. Logistic Regression
C2. k-Nearest Neighbors
C3. Classification Metrics
C4. ROC, PR & Imbalanced Data
C5. Support Vector Machines
C6. Cross-Validation & Data Leakage
M1. Decision Trees
M2. Random Forest
M3. Gradient Boosting
M4. Model Selection
Extensions
Extensions
E1. Hierarchical Clustering
E2. Text Representation
E2. Text Representation
Overview
E2.1 Bag-of-Words
E2.2 TF-IDF
E2.3 Word Embeddings
E2.4 Sentence Embeddings
E3. Topic Modeling & BERTopic
E3. Topic Modeling & BERTopic
Overview
E3.1 LDA
E3.2 NMF
E3.3 BERTopic
E3.4 Comparing the Models
E4. Neural Networks
E5. Explainability
E6. AutoML
E7. MLOps
E8. The Frontier
Project
Simulator
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Support
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Study Cards
Interview
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Overview
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Concept Map: Machine Learning
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