Please enable JavaScript.
Coggle requires JavaScript to display documents.
ML Fundamentals, This Mind Map is based on: - Coggle Diagram
ML Fundamentals
Special Concepts
Probability vs Likelihood
Maximum Likelihood
Gaussians (Univariate and Multivariate)
Expected Value and Variance
Entropy vs Cross Entropy
KL Divergence
Softmax vs Sigmoid
Generative vs Discriminative
The Curse of Dimensionality
Decision Boundaries
Inductive vs Deductive
LDA - QDA
Building ML Systems
Cross Validation
Bias/Variance Trade off
Gradient Descent
Regularization
Evaluation Metrics
Classification
Accuracy
Precision
Recall
F1-Score
ROC
AUC
Type I and II error
Learning Curves and Error Analysis
Ceiling Analysis
Supervised Learning
Linear Regression
Simple Linear Regresion
Multiple Linear Regression*
Polynomial Regression
5 assumptions before starting with LR
Logistic Regression
Ridge L2-Regularization
Lasso L1-Regularization
Elastic Net
Standard NNs
Support Vector Machine
self-learning
Polynomial Kernel
Radial Kernel
Naive Bayes Classifier
K-Nearest Neighbors
Unsupervised Learning
K-means
K-medoids
Fuzzy C-Means Clustering
PCA
SVD and PCA
Anomaly Detection
tSNE Embeddings
Recommender Systems
Hierarchical Clustering
Ensemble Methods
Bagging
Random Forests
XGBoost
Ligt GBM
Cat Boost
Boosting
Ada Boost
Decision Trees
Regression Decision Trees
Classification Decision Trees
Butterfly Effect and Overfit Problem
This Mind Map is based on:
Coursera: Machine Learning
DatA414 Course
StatQuest
Cheat Sheets ML
My Repository:
ML Review