Yousef's Notes
/
ML
Accuracy
AdaBoost
Adaptive Synthetic Sampling Method (ADASYN)
ADASYN
Adversarial Validation
Almost Correct Prediction Error Rate
AUC-ROC
AutoEncoder
Backpropagation
Bag of Words
Bagging
Baseline
Bias-Variance Tradeoff
Boruta
Building a Pipeline
Causes of Data Leakage
Class Weighting
Clustering
Cohen's Kappa Statistic
Common Problems with Data
Concept Drift
Confusion Matrix
Convolutional Neural Networks
Cross-Entropy Loss
Cross-Validation
Cumulative Gain
Data Augmentation
Data Bias
Data Imputation
Data Leakage
Data Manipulation Best Practices
Data Noise
Data Partitioning
Data Sampling
Dealing with Missing Attributes
Decision Tree
Deep Learning
Deep Learning Optimization Algorithms
Deep Learning Strategy
Defining the Goal
Density-Based Spatial Clustering of Applications with Noise
Dimensionality Reduction
Discounted Cumulative Gain
Distribution Shift
Ensemble Methods
Ensemble of Resampled Datasets
Entropy
Estimating Complexity
F-Score
Feature Engineering
Feature Hashing
Feature Scaling
Feature Selection
Feature Vector
Feedback Loop
Feedforward Neural Networks
Gaussian Mixture Models
Gini Index
Good Data
Gradient Boosting Machines
Gradient Descent
Grid Search
Handling Multiple Inputs and Outputs
Hierarchical Clustering
Hinge Loss
Homoscedasticity
Hyperplane
Hypterparameter Tuning
Ideal Discounted Cumulative Gain
Imbalanced Data
Information Gain
Interaction Data
Isomap
K-Means Clustering
k-Nearest Neighbors
L1-Regularization (Lasso Regression)
L2 Regularization (Ridge)
Laplace Smoothing
Latent Dirichlet Allocation (LDA)
Latent Semantic Analysis (LSA)
Linear Regression
Logistic Regression
Long Short-Term Memory
Loss Function
Machine Learning
Machine Learning Engineering
Machine Learning Team
Mean Absolute Error
Mean Average Precision
Mean Encoding
Mean Squared Error
Median Absolute Error
ML Cost
ML Impact
Model Calibration
Model Performance Metrics
Model-Based vs Instance-Based Learning
Multicollinearity
Naive Bayes
Neural Networks
Non-convex Optimization Problems
Normalization
Normalized Discounted Cumulative Gain
Notation
One-Hot Encoding
Outliers
Overfitting
Oversampling
Parameter Initialization
Parameters and Hyperparameters
Precision
Precision-Recall Tradeoff
Predictive Power
Principal Component Analysis
Principal Component Analysis 1
Properties of a Successful Model
Properties of Good Features
Questions about Data
R-squared
Random Forest
Raw and Tidy Data
Recall
Receiver Operating Characteristic
Recommender Systems Handbook
Recurrent Neural Networks
Regularization
Reinforcement Learning
Selecting the Learning Algorithm
Semi-Supervised Learning
Shallow Learning
Shallow vs Deep Learning
Singular Value Decomposition (SVD)
Spectral Clustering
Stacking
Stacking Features
Standardization
Stochastic Gradient Descent
Storing and Documenting Features
Storing Data
Supervised Learning
Support Vector Machine
Support Vector Machines
Syllabus
Synthesizing Features
Synthetic Minority Oversampling Technique (SMOTE)
t-SNE
The Curse of Dimensionality
Tokenization
Tomek Links
Topic Modelling
Training and Holdout Datasets
Transfer Learning
Transformers
Underfitting
Undersampling
Unsupervised Learning
Untitled
When to (not) Use ML
Why ML Projects Fail
Shallow vs Deep Learning
Shallow vs Deep Learning
Shallow Learning
Deep Learning
Shallow Learning
Singular Value Decomposition (SVD)
Shallow Learning