Learning Journey
Machine Learning - Complete topics to unlock ml algorithms
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Topics
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ML Algorithms
70%
đ
Vectors & Notation
đ
KNN
đ¯
K-Means
âĻ
Matrices
Ã
Matrix Multiplication
âī¸
Systems of Linear Equations
âŠī¸
Matrix Inverse
đ
Linear Regression
đ
Eigenvalues & Eigenvectors
đ
PCA
đēī¸
t-SNE/UMAP
đ˛
What is Probability?
|
Conditional Probability
đ
Bayes' Theorem
đ§
Naive Bayes
đŗ
Decision Trees
đ
Hidden Markov
X
Random Variables
E
Expectation & Variance
đ
Common Distributions
L
Maximum Likelihood Estimation
đēī¸
MAP Estimation
đ˛
Bayesian Regression
đ
Gaussian Processes
đĸ
Joint Distributions
âī¸
Covariance & Correlation
đŽ
GMM
đ
Functions & Graphs
đ¯
Limits & Continuity
đ
Derivatives
đ
Derivative Rules
â
Taylor Series
đ
RBF & Kernels
đ
Polynomial Reg
đ
Chain Rule (Multivariate)
đ§
Neural Networks
â
Partial Derivatives
â
The Gradient
H
Jacobian & Hessian
đ¯
Newton's Method
đ¯
What is Optimization?
âŦī¸
Gradient Descent
đ¯
Logistic Regression
Îą
Learning Rate
đ˛
Stochastic Gradient Descent
đ
Momentum
đ
Adam Optimizer
đ
Learning Rate Schedules
đĨ
Deep Learning
đŧī¸
CNN
đ
RNN
đ¤
Transformers
đ
Autoencoders
â
Convexity
âĄ
SVM
â
Convergence & Stopping
đ
Boosting
đ˛
Random Forest
đŋ
Hierarchical
đĄī¸
Regularization
đī¸
Ridge & Lasso
Available
Done
Locked
ML Algorithms
Mastered
From Core