Phase01-Introduction to machine learning

[MahdiEsrafili]

• [ ] Section 0: Install Anaconda and Jupyter Notebook

• [ ] Section 1: Regression

  • [ ] Boston Housing EDA
  • [ ] Do coding for Linear Regression using sk-learn
  • [ ] Answer this: How Linear Regression learns? [FILL HERE WITH RIGHT ANSWERS]

• [ ] Section 2: Classification

  • [ ] Logistic Regression
  • [ ] pima-indians-diabetes-database EDA
  • [ ] Model training
  • [ ] Answer this: what is similarity and difference of Logistic and Linear Regression? [FILL HERE WITH RIGHT ANSWERS]
  • [ ] KNN
  • [ ] KNN regression
  • [ ] KNN classification
  • [ ] Answer this: what is the main idea of KNN? [FILL HERE WITH RIGHT ANSWERS]
  • [ ] Answer this: is KNN good for large data? why? [FILL HERE WITH RIGHT ANSWERS]
  • [ ] SVM
  • [ ] SVM regression
  • [ ] SVM classification
  • [ ] Answer this: what is the main idea of SVM? [FILL HERE WITH RIGHT ANSWERS]
  • [ ] Decision Tree
  • [ ] DT regression
  • [ ] DT classification
  • [ ] Answer this: Compare KNN and SVM [FILL HERE WITH RIGHT ANSWERS]

• [ ] Section 3: Clustering

  • [ ] KMeans
  • [ ] EDA
  • [ ] Train model
  • [ ] Answer this: what is the KMeans main idea? [FILL HERE WITH RIGHT ANSWERS]
  • [ ] Answer this: what is similarity and difference of KMeans and KNN? [FILL HERE WITH RIGHT ANSWERS]

• [ ] Section 4: Ensemble Learning

  • [ ] regression with ensemble learning
  • [ ] classification with ensemble learning

• [ ] Section 5: Model comparison

  • [ ] Regression models comparison
  • [ ] Classification models comparison