Zbiór informacji o tym co każdy młody adept ML i DataScience wiedzieć powinien. Temat jest rozległy i nie mam jeszcze pomysłu jak to dobrze ustrukturyzować, ale będę próbował.

Newsletter Uczymy Maszyny

Mathematics

• Linear Algebra (Vectors; Norm of a vector; Matrices; Transpose of a matrix; The inverse of a matrix; The determinant of a matrix; Trace of a Matrix; Dot product; Eigenvalues; Eigenvectors)
• Probability Theory and Statistics (Mean, Median, Mode, Standard deviation/variance, Correlation coefficient and the covariance matrix, Probability distributions (Binomial, Poisson, Normal), p-value, Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve), Central Limit Theorem, R_2 score, Mean Square Error (MSE), A/B Testing, Monte Carlo Simulation)
• Multivariable Calculus (unctions of several variables; Derivatives and gradients; Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function; Cost function; Plotting of functions; Minimum and Maximum values of a function)
• Optimization Methods (Cost function/Objective function; Likelihood function; Error function; Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm))

ML Roadmap

A Tour of Machine Learning Algorithms

Obrazkowa mapa: ml-engineer-roadmap

Learning Style

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning

Machine Learning Algorithms

• Regression Algorithms
  • Ordinary Least Squares Regression (OLSR)
  • Linear Regression
  • Logistic Regression
  • Stepwise Regression
  • Multivariate Adaptive Regression Splines (MARS)
  • Locally Estimated Scatterplot Smoothing (LOESS)
• Instance-based Algorithms
  • k-Nearest Neighbor (kNN)
  • Learning Vector Quantization (LVQ)
  • Self-Organizing Map (SOM)
  • Locally Weighted Learning (LWL)
  • Support Vector Machines (SVM)
• Regularization Algorithms
  • Ridge Regression
  • Least Absolute Shrinkage and Selection Operator (LASSO)
  • Elastic Net
  • Least-Angle Regression (LARS)
• Decision Tree Algorithms
  • Classification and Regression Tree (CART)
  • Iterative Dichotomiser 3 (ID3)
  • C4.5 and C5.0 (different versions of a powerful approach)
  • Chi-squared Automatic Interaction Detection (CHAID)
  • Decision Stump
  • M5
  • Conditional Decision Trees
• Bayesian Algorithms
  • Naive Bayes
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • Averaged One-Dependence Estimators (AODE)
  • Bayesian Belief Network (BBN)
  • Bayesian Network (BN)
• Clustering Algorithms
  • k-Means
  • k-Medians
  • Expectation Maximisation (EM)
  • Hierarchical Clustering
• Association Rule Learning Algorithms
  • Apriori algorithm
  • Eclat algorithm
• Artificial Neural Network Algorithms
  • Perceptron
  • Multilayer Perceptrons (MLP)
  • Back-Propagation
  • Stochastic Gradient Descent
  • Hopfield Network
  • Radial Basis Function Network (RBFN)
• Deep Learning Algorithms
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Networks (RNNs)
  • Long Short-Term Memory Networks (LSTMs)
  • Stacked Auto-Encoders
  • Deep Boltzmann Machine (DBM)
  • Deep Belief Networks (DBN)
• Dimensionality Reduction Algorithms
  • Principal Component Analysis (PCA)
  • Principal Component Regression (PCR)
  • Partial Least Squares Regression (PLSR)
  • Sammon Mapping
  • Multidimensional Scaling (MDS)
  • Projection Pursuit
  • Linear Discriminant Analysis (LDA)
  • Mixture Discriminant Analysis (MDA)
  • Quadratic Discriminant Analysis (QDA)
  • Flexible Discriminant Analysis (FDA)
• Ensemble Algorithms
  • Boosting
  • Bootstrapped Aggregation (Bagging)
  • AdaBoost
  • Weighted Average (Blending)
  • Stacked Generalization (Stacking)
  • Gradient Boosting Machines (GBM)
  • Gradient Boosted Regression Trees (GBRT)
  • Random Forest