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Notes (work in progress)

Current focus is on programming -> elements_prog_interview

  • databases

    • Databases and SQL for Data Science, IBM, Coursera

  • deep_learning

    • "TensorFlow for Deep Learning (O'Reilly)", Bharath Ramsundar & Reza Bosagh Zadeh
    • "Fundamentals of Deep Learning (O'Reilly)", Nikhil Buduma with contributions by Nicholas Locascio

  • interview_questions

  • linear_algebra

    • Some introductory concepts and python representations

  • machine_learning

    • harvard_cs109.ipynb: CS109 Data Science, Harvard University
    • sup_learning_scikit_learn.ipynb: Supervised Learning with scikit-learn, DataCamp
    • advanced_machine_learning_tensorflow_gcp: Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera
    • machine_learning_tensorflow_gcp:Machine Learning with TensorFlow on Google Cloud Platform Specialization, Coursera
    • aws_ml_path_data_science: AWS Machine Learning Path, Data Scientist

  • programming

    • data_structures_and_algorithms
      • "Programming in Python 3, A Complete Introduction to the Python Language", Mark Summerfield
      • "Elements of Programming Interview in Python", Adnan Aziz, Tsung-Hsien Lee, Amit Prakash
      • Python3 documentation
      • Learn to Program: Crafting Quality Code, University of Toronto, Coursera
    • elements_prog_interview: Chapter overview + coding exercises of Elements of Programming Interviews in Python by Adnan Aziz, Tsung-Hsien Lee, Amit Prakash
    • interview_practice: leetcode, mock interviews
    • python_programmer_track_datacamp: Python Programmer Track, DataCamp
    • debugging_testing_profiling.ipynb: Notes from Crafting Quality Code, University of Toronto, Coursera: https://www.coursera.org/learn/program-code/home/welcome
    • mit_datastructures_algos.ipynb: MIT 6.006 Introduction to Algorithms, Fall 2011, https://www.youtube.com/watch?v=HtSuA80QTyo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=1

  • statistics

    • foundations_of_statistics.ipynb: Random Variables, Sampling Distributions, Confidence Intervals, Khan Academy
    • infer_statistics_python.ipynb: Statistical Thinking in Python (Part 1), DataCamp

  • quick_notes.ipynb

    • Random notes