Best Structure to start exploring Machine Learning Algorith



Hi Friends,

I am new to this data science domain. I have recently completed statistics course from Udacity and have gone through various courses/ article for pre model stage like data exploration, feature engineering to understand the data in detail. Now for next step, I want to explore machine learning algorithms and there are enough methods available( [list is exhaustive][1]). I am not able to structure these algorithms as level1, level2, level3 …

Please help me to structure these algorithm as level1, level2, level3…so that a beginner can start with level1 and by the end of level 3/4, he/ she is able to solve any data science challenge.




You can start with these algorithms

  1. linear regresion
  1. logistic regression
  2. decision trees
  3. random forests
  4. support vector machines
  5. k-means clustering
  6. naive bayes
  7. artificial neural network

after these basic algorithms, you can move on to more complex algorithms and improvised versions of these basic algorithms.

hope this gives you a start!


This may not be the answer that you are looking for however, you can start working this way:-