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