Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• 

  Introduction and Logistics
  tl;dr: Introduction to the course and overview
  [slides] [students_notes]
  

  Suggested Readings:

  • What is Data Mining
  • GeeksforGeeks Tutoria
• 

  Python bootstrap
  tl;dr: Dive deeper on the Python syntax
  [notes] [student_notes]
  

  Suggested Readings:

  • Official Python documentation

  • Real Python

  • Python Crash Course

  • Full Stack Python

• 

  Numpy and Pandas
  tl;dr: Tutorial on using Numpy and Pandas
  [notes]
  

  Suggested Readings:

  • NumPy Official Documentation
  • NumPy Quickstart Tutorial
  • Pandas Official Documentation
  • Pandas Getting Started Guide
• 

  Data and Preprocessing
  tl;dr: How to represent data and preprocess it before starting any analysis.
  [notes]
  

  Suggested Readings:

  • Types of Data

  • Khan Academy Type of Data

• 

  Data Similarity and Dissimilarity measures
  tl;dr: Learn about some distances between the data and how to process the dat
  [notes]
  

  Suggested Readings:

  • Types of Data

  • Khan Academy Type of Data

• 

  Classification Decision Trees
  tl;dr: Learn about the details for decision trees
  [slides]
  

  Suggested Readings:

  • Types of Data

  • Khan Academy Type of Data

• 

  Classification with Support Vector Machine
  tl;dr: SVM Classifier
  [notes]
  

  Suggested Readings:

  • SVM machine learning
  • Geek for geeks Tutorial
  • Idiot Guide to SVM
• 

  Classification with Naive Bayes
  tl;dr: naive Bayes
  [slides]
  

  Suggested Readings:

  • Naive Bayes and Descriminative classifier
  • Naive Bayes Sklearn
  • Naive Bayes for spam classification
• 

  Clustering Methods
  tl;dr: naive Bayes
  [slides]
  

  Suggested Readings:

  • Geek for Geek Tutorial
  • 8 clustering alrgorithms
  • In depth guide