/Machine-learning

Machine Learning: Numpy; Pandas; Scikit-Learn; Scipy; Keras

Primary LanguageJupyter Notebook

Machine Learning

Table of contents

  • Machine learning - steps and concepts
  1. Data science life cycle
  2. Machine learning operations (MLOps)
  3. Automate machine learning via FLAML
  4. Supervised learning (regression - classification)
    1. Regression (Linear, Ridge/LASSO, Polynomial, SVM, K-NN)
    2. Classification (K-NN, Logistic regression, SVM, Kernelized SVM, Decision Trees, Naive Bayes Classifiers,
    3. Ensemble models
      1. XGBoost
      2. LightGBM
      3. Random forest
  5. Un-supervised learning
    1. Transformation
      1. Dimensionality Reduction: PCA
      2. Manifold learning: MDS and t-NSE
    2. Clustering
      1. K-means clustering
      2. Agglomerative clustering
      3. DBSCAN
      4. K-mode clustering
      5. K-prototype clustering
      6. Evaluate clustering algorithm

Refernces