Machine Learning

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  1. Basics
    1. Supervised,unsupervised,reinforcement
    2. Bias-variance trade-off
    3. Overfitting, underfitting
  2. Gradient descent:-batch,stochastic
  3. Linear discriminant analysis (LDA)
  4. Principal Component Analysis(PCA)
  5. Learning Vector Quantization (LVQ)
  6. Regularization methods:- Ridge,LASSO
  7. Kernel smoothing methods
  8. Ensemble learning:-Bagging(bootstrap aggregation),boosting,stacking,blending
  9. Ordinary least squares
  10. Partial Least squares
  11. Kernel density Estimation
  12. Radial basis functions
  13. AIC,BIC
  14. K-fold cross validation
  15. Generalized Additive Models (GAMs)
  16. Multivariate Adaptive Regression Splines(MARS)
  17. Gradient boosting
  18. NLP
    1. Word sense disambiguation
    2. Pronoun resolution
    3. Machine translation
    4. Tokenization
    5. Regular expressions
    6. Stemming
    7. Lemmatization
  19. Evaluation metrics
    1. AUC
    2. Precision
    3. Recall
    4. Specificity
    5. Mean absolute percentage error
    6. Root mean square error
  20. Algorithms
    1. Linear regression: Usually performed through OLS
    2. Logistic regression
    3. Naive Bayes
    4. K-Nearest Neighbors
    5. K means clustering
    6. Classification and regression trees(CARTs)
    7. Support vector machines
    8. AdaBoost
    9. Random forest
    10. ARIMA
    11. Decision Trees
    12. ID3
    13. CHAID
    14. C4.5, C5.0
    15. Hierarchical Clustering
  21. Miscellaneous
    1. Curse of dimensionality
    2. No free lunch theorem
    3. Occams Razor
  22. Deep Learning
    1. Neural Networks
    2. Bayesian neural nets
    3. Deep Boltzmann Machine(DBM)
    4. Deep Belief Networks(DBN)
    5. Convolutional Neural Networks
  23. Mathematics
    1. Hypothesis testing
    2. scedasticity
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User Avatar vikash

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