Skip to content

Latest commit

 

History

History
247 lines (211 loc) · 12 KB

References.md

File metadata and controls

247 lines (211 loc) · 12 KB
Raw

TOC

This repository is a collection of notes developed from a curated list of courses, books, articles, and videos. If you find any of it useful, please feel free to use it.

CDSquaredNotes

  • Data Engineering

  • General Non-Technical Books

    • The master algorithm
    • Programming Collective Intelligence

  • Deep Learning

    • [Notes for Deep Learning Course by Kaggle]
    • [Deep Learning with Python by Francois Chollet]
    • [Deep Learning Part 1: Practical Deep Learning for Coders by Fast.ai]
    • [Deep Learning Part 2: Cutting Edge Deep Learning For Coders by fast.ai]
    • Neural Networks and Deep Learning from Deeplearning.ai
    • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization from Deeplearning.ai
    • Structuring Machine Learning Projects from Deeplearning.ai
    • Convolutional Neural Networks from Deeplearning.ai
    • Sequence Models from Deeplearning.ai

  • Data Science Languages

    • [R by Kaggle]
    • The Data Scientist’s Toolbox from Duke University on Coursera
    • R Programming from Duke University on Coursera
    • Getting and Cleaning Data from Duke University on Coursera
    • Exploratory Data Analysis from Duke University on Coursera
    • Reproducible Research from Duke University on Coursera
    • Statistical Inference from Duke University on Coursera
    • Regression Models from Duke University on Coursera
    • Practical Machine Learning from Duke University on Coursera
    • Developing Data Products from Duke University on Coursera
    • Data Science Capstone from Duke University on Coursera
    • Introduction to Data Science in Python from University of Michigan on Coursera
    • Applied Plotting, Charting & Data Representation in Python from University of Michigan on Coursera
    • Applied Machine Learning in Python from University of Michigan on Coursera
    • Applied Text Mining in Python from University of Michigan on Coursera
    • Applied Social Network Analysis in Python from University of Michigan on Coursera
    • R Fundamentals
    • Exploring Topics in Data Science from Dataquest
    • Data Scientist Capstone

  • Data Visualizations

    • [Data Visualizations by Kaggle]

  • Big Data

    • Introduction to Big Data from University of California-San Diego on Coursera
    • Big Data Modeling and Management Systems from University of California-San Diego on Coursera
    • Big Data Integration and Processing from University of California-San Diego on Coursera
    • Machine Learning With Big Data from University of California-San Diego on Coursera
    • Graph Analytics for Big Data from University of California-San Diego on Coursera
    • Big Data - Capstone Project from University of California-San Diego on Coursera
    • Big Data Essentials: HDFS, MapReduce and Spark RDD by Yandex on Coursera
    • Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames by Yandex on Coursera
    • Big Data Applications: Machine Learning at Scale by Yandex on Coursera
    • Big Data Applications: Real-Time Streaming by Yandex on Coursera
    • Big Data Services: Capstone Project by Yandex on Coursera
    • Spark and Map-Reduce from Dataquest
    • Processing Large Datasets In Pandas from Dataquest
    • Optimizing Code Performance On Large Datasets from Dataquest
    • Big Data: Principles and Best Practices of Scalable Realtime Data Systems
    • Hadoop: The Definitive Guide
    • Learning Spark

  • Data Engineering

    • [AWS Data Pipeline by Amazon Web Services]
    • Postgres for Data Engineers
    • Optimizing Postgres Databases
    • Building a Data Pipeline

  • Databases

  • Data Science Basics

    • [The Data Science Handbook by Field Cady, Wiley] Summary: The data science handbook goal is to prepare readers of various skill levels to be compotent data scientist, complete a data project, or become what the book calls a "Data unicorns". The book is broken down into three sections: 1. The stuff you'll always use part equip a reader with skills to solve simple data problems and covers subjects likely to arise in any data project 2. Stuff you still need to know cover a range of topics which a professional data scienctist will likely use during their career. 3. Specialized or advanced topics covers topics in greater depth from part 1 and 2, and moves from nuts and bolts applications to more abstract theory

  • Quantitative Foundations

    • [Handbook of Mathematics by Springer]
    • Calculus For Machine Learning from Dataquest
    • Linear Algebra For Machine Learning from Dataquest
    • Khan Linear Algebra
    • Khan Differential Calculus
    • Khan Integral Calculius
    • Khan Multivariable Calculus
    • Mathematics for Machine Learning MIT
    • Single Variable Calculus MIT
    • Multivariable Calculus MIT
    • Introduction to Probability and Statistics MIT
    • Calculus of Several Variables MIT
    • Mathematics for Computer Science (Fall 2005) MIT
    • Advanced Calculus for Engineers (Fall 2004) MIT
    • Linear Algebra - Communications Intensive (Spring 2004) MIT
    • Real Analysis (Fall 2012) MIT
    • Automata, Computability, and Complexity (Spring 2011) MIT
    • Theory of Computation (Fall 2006) MIT

  • Quantum Computing

    • [Quantum Computation and Quantum Information by Isaac Chuang and Michael Nielsen]
    • Quantum computing for the Determined
    • An introduction to quantum machine learning
    • Introduction to Quantum Mechanics I
    • Introduction toQuantum Mechanic II
    • Physics I: Classical Mechanics (Fall 2008)
    • Physics II: Electricity and Magnetism (Spring 2007)
    • Physics III (Spring 2003)
    • Quantum Physics I (Spring 2016)
    • Quantum Physics II (Fall 2013)
    • Quantum Physics III (Spring 2016)
    • Quantum Computation (Fall 2003)
    • Semantic Techniques in Quantum Computation – Editors Simon Gay and Ian Mackie
    • Grover L.K.: A fast quantum mechanical algorithm for database search, Proceedings, 28th Annual ACM Symposium on the Theory of Computing, (May 1996) p. 212

  • Machine Learning

    • [Notes for Machine Learning by Kaggle]
    • Machine Learning taught by Andrew Ng on Cousera
    • Machine Learning Foundations: A Case Study Approach from the University of Washington on Coursera
    • Machine Learning: Regression from the University of Washington on Coursera
    • Machine Learning: Classification from the University of Washington on Coursera
    • Machine Learning: Clustering & Retrieval from the University of Washington on Coursera
    • Introduction to Deep Learning from Higher School of Economics, National Research University on Coursera
    • How to Win a Data Science Competition: Learn from Top Kagglers from Higher School of Economics, National Research University on Coursera
    • Bayesian Methods for Machine Learning from Higher School of Economics, National Research University on Coursera
    • Natural Language Processing from Higher School of Economics, National Research University on Coursera
    • Practical Reinforcement Learning from Higher School of Economics, National Research University on Coursera
    • Deep Learning in Computer Vision from Higher School of Economics, National Research University on Coursera
    • Addressing Large Hadron Collider Challenges from Higher School of Economics, National Research University on Coursera
    • Machine Learning for Hackers
    • Machine Learning by Tom M Mitchell
    • Pattern Recognition and Machine Learning
    • Linear Regression For Machine Learning from Dataquest
    • Machine Learning in Python: Intermediate from Dataquest
    • Decision Trees from Dataquest
    • Machine Learning Project from Dataquest

  • Statistical Machine Learning

    • Probabilistic Graphical Models 1: Representation
    • Probabilistic Graphical Models 2: Inference
    • Probabilistic Graphical Models 3: Learning
    • The elements of statistical learning
    • Probability and Statistics in Python: Beginner from Dataquest
    • Probability and Statistics in Python: Intermediate from Dataquest

  • Python

    • Python Programming: Beginner from Dataquest
    • Python Programming: Intermediate from Dataquest
    • Python Programming: Advanced from Dataquest

  • Ruby

  • Scala

    • Functional Programming Principles in Scala from Ecole Polytechnique Federale De Lausanne on Coursera
    • Functional Program Design in Scala from Ecole Polytechnique Federale De Lausanne on Coursera
    • Parallel programming from Ecole Polytechnique Federale De Lausanne on Coursera
    • Big Data Analysis with Scala and Spark from Ecole Polytechnique Federale De Lausanne on Coursera
    • Functional Programming in Scala Capstone from Ecole Polytechnique Federale De Lausanne on Coursera

  • JavaScript

  • SQL

    • SQL Fundamentals from Dataquest
    • SQL Intermediate: Table Relations and Joins from Dataquest
    • SQL and Databases: Advanced from Dataquest
    • SQL Basics by Mode Analytics
    • SQL Intermediate by Mode Analytics
    • SQL Advanced by Mode Analytics

  • Robotics

  • Command Line

    • Command Line: Beginner from Dataquest
    • Command Line: Intermediate from Dataquest

  • Git and Version Control

    • Git and Version Control from Dataquest

  • Web Development

  • Exploratory Data Analysis

    • Data Analysis with Pandas: Intermediate from Dataquest
    • Exploratory Data Visualization from Dataquest
    • Storytelling Through Data Visualization from Dataquest

  • Data Cleaning

    • Data Cleaning from Dataquest

  • API

    • APIs and Web Scraping from Dataquest
    • Machine Learning Fundamentals from Dataquest

  • Natural Language Processing

    • Natural Language Processing

  • Data Structures and Algorithms

    • Data Structures and Algorithms from Dataquest
    • Algorithms And Data Structures
    • Recursion and Trees

Competitions

  • Kaggle Fundamentals from Dataquest

  • Chatbots

[Deep Learning by Kaggle] (https://www.kaggle.com/learn/deep-learning)

Summary: Deep learning by Kaggle introduces concepts of Deep Learning using TensorFlow and Keras through an application based method while analyzing distinct parts.