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Machine-Learning-A-Z

As part of #100DaysofMLCode Challenge, I have started to strengthen my foundation in Machine Learning with the course "Machine learning A-Z" on Udemy.

Topics covered so far:

REGRESSION:

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression
  • Decision Tree Regression
  • Random Forest Regression

CLASSIFICATION:

  • Logistic Regression
  • K-Nearest Neighbor
  • Support Vector Machines
  • Kernel SVM
  • Naive Bayes
  • Decision Tree
  • Random Forest

CLUSTERING:

  • K-Means
  • Hierarchical

ASSOCIATION RULE LEARNING

  • Apriori
  • Eclat

REINFORCEMENT LEARNING

  • Upper Confidence Bound
  • Thompson Sampling

NATURAL LANGUAGE PROCESSING

  • Bag of words model

DEEP LEARNING

  • Artificial Neural Networks
  • Convolutional Neural Networks

DIMENSIONALITY REDUCTION

  • Principal Component Analysis
  • Linear Discrimant Analysis
  • Kernel PCA