Targeted Study

Books

• Machine Learning a Probablistic Perspective - Kevin Murphy [pdf]
• Pattern recognition and machine learning - Bishop [pdf]
• Introduction to Statistical Learning - Hastie and Tibshirani [pdf]
• Elements of Statistical Learning - Hastie and Tibshirani [pdf]
• Larry Wasserman - All of Statistics [pdf]

Advanced

• Understanding Machine Learning: From Theory to Algorithms - Shai Ben David [pdf]
• High-Dimensional Probablity - Vershynin [pdf]
• Foundations of Data Science - Blum/Hopcroft/Kannan [pdf]
• Convex Optimization - Boyd [pdf]

Courses

   CS229 Machine Learning - Stanford - Ng //    Notes pdfs
   Convex Optimization - Stanford - Boyd
  18-657 Mathematics for Machine Learning
  CS109 Data Science - Harvard
  CS181 Machine Learning - Harvard
  CS182 Artificial Intelligence - Harvard
  CS281 Advanced Machine Learning - Harvard

  Neural networks - youtube - 3Blue1Brown
  Piazza link

Stanford

MIT

Harvard

MOOCs

   UDEMY - Jose Portilla - Data Science/ML
   UDEMY - Jose Portilla - Python
   UDEMY - Kirill Eremenko - Machine Learning
   UDEMY - Kirill Eremenko - Data Science
   UDEMY - Data Science Bootcamp
   COURSERA - Andrew Ng - Machine Learning
   COURSERA - Andrew Ng - Deep Learning
   MICRSOFT - Data Science - AI
   GOOGLE - Data Scienctist
   FAST.Ai - ML-Deep Learning
https://www.edx.org/micromasters/mitx-statistics-and-data-science https://www.edx.org/professional-certificate/harvardx-data-science