Colab

Lingo:

• Notebook Document (.ipynb file): The actual file that you create and save. It's a JSON file that stores the content of your notebook, including code cells, text cells (Markdown), and the output of executed code.
• Cells: The building blocks of a notebook.
• Code Cells: These contain code to be executed by the kernel.
• Markdown Cells: These contain text formatted using Markdown, which is used for narrative text, documentation, and including images or links.
• Kernel: The "computational engine" that executes the code contained in a notebook.
• Jupyter Server: A backend server that communicates with the browser and the kernel.
• Dashboard: The initial page you see when you launch Jupyter Notebook, used for managing your notebooks.

Documenting colab materials:

• Investigations
• Templates
• Projects

Colab

Be able to experiment in a notebook

• Article: Securely storing configuration credentials in a Jupyter Notebook
• Article: Automatically Reload Modules with %autoreload
• Calmcode: ipywidgets
• Documentation: Jupyter Lab
• Pluralsight: Getting Started with Jupyter Notebook and Python
• Youtube: William Horton - A Brief History of Jupyter Notebooks
• Youtube: I Like Notebooks
• Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
• Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
• Youtube: nbdev live coding with Hamel Husain
• Youtube: How to Use JupyterLab

Be able to visualize data

• Article: Creating a Catchier Word Cloud Presentation
• Article: Effectively Using Matplotlib
• Article: Which color scale to use when visualizing data
• Datacamp: Introduction to Data Visualization with Python
• Datacamp: Introduction to Seaborn
• Datacamp: Introduction to Matplotlib
• Datacamp: Intermediate Data Visualization with Seaborn
• Datacamp: Visualizing Time Series Data in Python
• Datacamp: Improving Your Data Visualizations in Python
• Datacamp: Visualizing Geospatial Data in Python
• Datacamp: Interactive Data Visualization with Bokeh
• Youtube: Applied ML 2020 - 02 Visualization and matplotlib 1:07:30

Be able to import data from multiple sources

• Docs: Beautiful Soup Documentation
• Datacamp: Importing Data in Python (Part 2)
• Datacamp: Web Scraping in Python

Be able to setup data annotation efficiently

• Article: Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”
• Article: We need Synthetic Data
• Article: Weak Supervision for Online Discussions
• Article: ML Infrastructure Tools for Data Preparation
• Article: Exploring the Role of Human Raters in Creating NLP Datasets
• Article: Inter-Annotator Agreement (IAA)
• Article: How to compute inter-rater reliability metrics (Cohen’s Kappa, Fleiss’s Kappa, Cronbach Alpha, Krippendorff Alpha, Scott’s Pi, Inter-class correlation) in Python
• Article: The Pitfalls of Inter-Rater Reliability in Data Labeling and Machine Learning
• Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
• Youtube: Training a NER Model with Prodigy and Transfer Learning
• Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning
• Youtube: ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations
• Youtube: Active Learning: Why Smart Labeling is the Future of Data Annotation | Alectio
• Youtube: Lecture 8: Data Management (Full Stack Deep Learning - Spring 2021) 0:59:42
• Youtube: Lab 6: Data Labeling (Full Stack Deep Learning - Spring 2021) 0:05:06
• Youtube: Lecture 6: Data Management
• Youtube: SE4AI: Data Quality 1:07:15
• Youtube: SE4AI: Data Programming and Intro to Big Data Processing 0:33:04
• Youtube: SE4AI: Managing and Processing Large Datasets 1:21:27

Be able to manipulate data with Numpy

• Article: A Visual Intro to NumPy and Data Representation
• Article: Good practices with numpy random number generators
• Article: NumPy Illustrated: The Visual Guide to NumPy
• Article: NumPy Fundamentals for Data Science and Machine Learning
• Datacamp: Intro to Python for Data Science
• Pluralsight: Working with Multidimensional Data Using NumPy

Be able to manipulate data with Pandas

• Article: Visualizing Pandas' Pivoting and Reshaping Functions
• Article: A Gentle Visual Intro to Data Analysis in Python Using Pandas
• Article: Comprehensive Guide to Grouping and Aggregating with Pandas
• Article: 8 Python Pandas Value_counts() tricks that make your work more efficient
• Datacamp: pandas Foundations
• Datacamp: Pandas Joins for Spreadsheet Users
• Datacamp: Manipulating DataFrames with pandas
• Datacamp: Merging DataFrames with pandas
• Datacamp: Data Manipulation with pandas
• Datacamp: Optimizing Python Code with pandas
• Datacamp: Streamlined Data Ingestion with pandas
• Datacamp: Analyzing Marketing Campaigns with pandas
• edX: Implementing Predictive Analytics with Spark in Azure HDInsight
• Article: Modern Pandas
  • Modern Pandas (Part 1)
  • Modern Pandas (Part 2)
  • Modern Pandas (Part 3)
  • Modern Pandas (Part 4)
  • Modern Pandas (Part 5)
  • Modern Pandas (Part 6)
  • Modern Pandas (Part 7)
  • Modern Pandas (Part 8)

Be able to manipulate data in spreadsheets

• Datacamp: Spreadsheet basics
• Datacamp: Data Analysis with Spreadsheets
• Datacamp: Intermediate Spreadsheets for Data Science
• Datacamp: Pivot Tables with Spreadsheets
• Datacamp: Data Visualization in Spreadsheets
• Datacamp: Introduction to Statistics in Spreadsheets
• Datacamp: Conditional Formatting in Spreadsheets
• Datacamp: Marketing Analytics in Spreadsheets
• Datacamp: Error and Uncertainty in Spreadsheets
• edX: Analyzing and Visualizing Data with Excel

Algorithms

• Grokking Algorithms: An Illustrated Guide for Programmers and Other Curious People
• LECTURE 1: ALGORITHMIC THINKING, PEAK FINDING
• Article: Asymptotic Analysis Explained with Pokémon: A Deep Dive into Complexity Analysis
• Book: Grokking Algorithms
• Codecademy: Big O
• Khan Academy: Data Structures
• Udacity: Intro to Algorithms
• Udacity: Programming Languages

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