learning

A running log of things I'm learning to build strong core software engineering skills while also expanding breadth of knowledge on adjacent technologies a little bit everyday.

Core Skills

Business Understanding

Concept	Resource	Done
	Book: Delivering Happiness	✓
	Book: Good to Great: Why Some Companies Make the Leap...And Others Don't	✓
	Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams	✓
	Book: How Google Works	✓
	Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business	✓
	Book: Rework	✓
	Book: The Airbnb Story	✓
	Book: The Personal MBA	✓
	Udacity: How to Build a Startup
Marketing	Smartly: Marketing Fundamentals	✓
	Udacity: App Marketing
	Facebook: Digital marketing: get started	✓
	Facebook: Digital marketing: go further	✓
	Google Analytics for Beginners	✓
	Moz: The Beginner's Guide to SEO	✓
	Treehouse: SEO Basics	✓
	Udacity: App Monetization

Python Programming

Concept	Resource	Done
Language	Codecademy: Learn Python	✓
	Cognitiveclass.ai: Python for Data Science	✓
	Datacamp: Python for R Users	✓
	Datacamp: Python for Spreadsheet Users	✓
	Datacamp: Intro to Python for Finance	✓
	edX: Introduction to Python for Data Science	✓
	edX: Programming with Python for Data Science	✓
	Google's Python Class	✓
	Treehouse: Python Basics	✓
	TheNewBoston: Python Programming Tutorials	✓
	Book: A Byte of Python	✓
	Book: Learn Python The Hard way	✓
	Datacamp: Writing Efficient Python Code	✓
	Datacamp: Writing Functions in Python	✓
	Datacamp: Working with Dates and Times in Python
	Datacamp: Object-Oriented Programming in Python	✓
	Datacamp: Importing Data in Python (Part 1)	✓
	Datacamp: Intermediate Python for Data Science	✓
	Datacamp: Python Data Science Toolbox (Part 1)	✓
	Datacamp: Python Data Science Toolbox (Part 2)	✓
Standard Library	Article: A reverse chronology of some Python features	✓
	Article: No Really, Python's Pathlib is Great
	Article: A deep dive on Python type hints	✓
	Book: Python 201
	Book: The Python 3 Standard Library By Example
	Calmcode: logging	✓
	Calmcode: virtualenv	✓
	Calmcode: tqdm	✓
	Datacamp: Command Line Automation in Python
Regular Expression	Regex For Noobs (like me!) - An Illustrated Guide	✓
	Youtube: Python 3 Programming Tutorial - Regular Expressions / Regex with re	✓
	Youtube: Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)	✓
Concurrency	Article: Python Concurrency: The Tricky Bits	✓
	Article: Speeding Up Python with Concurrency, Parallelism, and asyncio
	Article: Speed Up Your Python Program With Concurrency
	Youtube: Python Concurrency and Multithreading
	Youtube: Aaron Richter- Parallel Processing in Python| PyData Global 2020
Packaging	Datacamp: Developing Python Packages	✓
	Datacamp: Conda Essentials	✓
	Datacamp: Conda for Building & Distributing Packages
	Article: Push and pull: when and why to update your dependencies
	Article: Reproducible and upgradable Conda environments: dependency management with conda-lock
	Article: Options for packaging your Python code: Wheels, Conda, Docker, and more
Project Organization	Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit	✓
	Book: Writing Idiomatic Python 3
	Article: Hypermodern Python
	Article: Hypermodern Python Chapter 2: Testing
	Article: Hypermodern Python Chapter 3: Linting
	Article: Hypermodern Python Chapter 4: Typing
	Article: pydantic	✓
	Article: Hypermodern Python Chapter 5: Documentation
	Article: Hypermodern Python Chapter 6: CI/CD
	Article: Stop using print, start using loguru in Python	✓
	Datacamp: Creating Robust Python Workflows	✓
	Datacamp: Software Engineering for Data Scientists in Python	✓
	Datacamp: Designing Machine Learning Workflows in Python	✓
	Youtube: Hydra configuration	✓

Data Structures and Algorithms

Concept	Resource	Done
	Book: Grokking Algorithms	✓
	Codecademy: Big O	✓
	Udacity: Intro to Data Structures and Algorithms
	Udacity: Intro to Algorithms

Linux & Command Line

Concept	Resource	Done
	Codecademy: Learn the Command Line	✓
	Datacamp: Introduction to Shell for Data Science	✓
	Datacamp: Introduction to Bash Scripting	✓
	Datacamp: Data Processing in Shell	✓
	Lecture 1: Course Overview + The Shell (2020) 0:48:16	✓
	Lecture 2: Shell Tools and Scripting (2020) 0:48:55	✓
	Lecture 3: Editors (vim) (2020) 0:48:26
	Lecture 4: Data Wrangling (2020) 0:50:03	✓
	Lecture 5: Command-line Environment (2020) 0:56:06	✓
	Lecture 7: Debugging and Profiling (2020) 0:54:13	✓
	Lecture 8: Metaprogramming (2020) 0:49:52	✓
	Lecture 9: Security and Cryptography (2020) 1:00:59
	Udacity: Linux Command Line Basics	✓
	Udacity: Shell Workshop	✓
	Udacity: Configuring Linux Web Servers	✓
	Article: Streamline your projects using Makefile	✓
	Article: Understand Linux Load Averages and Monitor Performance of Linux	✓
	Article: Command-line Tools can be 235x Faster than your Hadoop Cluster	✓
	Calmcode: makefiles	✓
	Calmcode: entr	✓

Version Control

Concept	Resource	Done
Git	Codecademy: Learn Git	✓
	Code School: Git Real	✓
	Datacamp: Introduction to Git for Data Science	✓
	Thoughtbot: Mastering Git
	Udacity: Version Control with Git	✓
	Lecture 6: Version Control (git) (2020) 1:24:59
	Youtube: Git & Scripting	✓
	Article: Mastering Git Stash Workflow
	Article: How to Become a Master of Git Tags	✓
	Article: Keep your git directory clean with git clean and git trash
GitHub	Udacity: GitHub & Collaboration	✓
	Udacity: How to Use Git and GitHub	✓
LFS	Youtube: 045 Introduction to Git LFS	✓
	Article: How to track large files in Github / Bitbucket? Git LFS to the rescue	✓

Code Editor / IDE

Concept	Resource	Done
PyCharm	Article: Work remotely with PyCharm, TensorFlow and SSH	✓
	Article: Docker as Remote Interpreter for PyCharm Professional	✓
	Article: Python remote debugging with PyCharm, CUDA, and Conda	✓
VSCode	Article: How To Use Visual Studio Code for Remote Development via the Remote-SSH Plugin	✓
	Youtube: Getting Started with Python in Visual Studio Code	✓
	Visual Studio Code Crash Course	✓
	Youtube: VSCode Keyboard Shortcuts For Productivity	✓
	Youtube: Getting Started with Jupyter Notebooks in VS Code	✓
	Youtube: Notebooks in VS Code Are Getting Revamped!	✓
	Youtube: Getting Started with PyTorch in VS Code	✓
	Youtube: What every GitHub user should know about VS Code - GitHub Satellite 2020	✓
	VS Code and GitHub	✓

Test-Driven Development

Concept	Resource	Done
Test Cases	Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)	✓
	Pluralsight: Test-driven Development: The Big Picture	✓
	Test Driven Development with Python
	Datacamp: Unit Testing for Data Science in Python	✓
	Article: How to cheat at unit tests with pytest and Black	✓
	Youtube: Lab 8: Testing and Continuous Integration (Full Stack Deep Learning - Spring 2021) 0:13:26	✓
	Article: 4 Lesser-Known Yet Awesome Tips for Pytest	✓
	Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
	Article: Unit Testing for Data Scientists	✓
ML	Article: Effective testing for machine learning systems	✓
	Youtube: Beyond Accuracy: Behavioral Testing of NLP Models with CheckList | AISC	✓
	Youtube: Lecture 10: ML Testing & Explainability (Full Stack Deep Learning - Spring 2021) 1:41:12

Web Technology

Concept	Resource	Done
Design	Book: Refactoring UI
	Code School: Fundamentals of Design	✓
	Thoughtbot: Design for Developers
	Udacity: Product Design
	Udacity: Rapid Prototyping
HTML	Codecademy: Learn HTML	✓
	Codecademy: Make a website	✓
	Treehouse: HTML	✓
CSS	Pluralsight: CSS Positioning	✓
	Pluralsight: Introduction to CSS	✓
	Pluralsight: CSS: Specificity, the Box Model, and Best Practices	✓
	Pluralsight: CSS: Using Flexbox for Layout	✓
	Code School: Blasting Off with Bootstrap	✓
	Pluralsight: UX Fundamentals	✓
	Codecademy: Learn SASS	✓
Javascript	Treehouse: Javascript Booleans	✓
	Udacity: ES6 - JavaScript Improved	✓
	Udacity: Intro to Javascript	✓
	Udacity: Object Oriented JS 1	✓
	Udacity: Object Oriented JS 2	✓
	(ES6) - Beau teaches JavaScript	✓
	Udemy: Understanding Typescript	✓
	Codecademy: Learn ReactJS: Part I	✓
	Codecademy: Learn ReactJS: Part II	✓
	Codecademy: Learn JavaScript	✓
	Codecademy: Jquery Track	✓
	Pluralsight: Using The Chrome Developer Tools	✓

Backend & Web Servers

Concept	Resource	Done
Theory	Udacity: Authentication & Authorization: OAuth
	Udacity: HTTP & Web Servers
	Udacity: Client-Server Communication
	Udacity: Designing RESTful APIs
	Udacity: Networking for Web Developers
FastAPI	Article: Microservice in Python using FastAPI
	Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI	✓
	Youtube: FastAPI from the ground up	✓
	Youtube: Python pydantic Introduction – Give your data classes super powers	✓
Gunicorn	Article: Selecting gunicorn worker types for different python web applications.	✓
	Article: Better performance by optimizing Gunicorn config	✓
Tensorflow Serving	Article: Understanding TensorFlow Serving
	Article: Serving models using Tensorflow Serving and Docker
Cortex	Youtube: PyData Vancouver meetup: cortex.dev : Serving machine learning models in production	✓
Celery	Article: Celery Execution Pools: What is it all about?	✓
	Article: Distill: Why do we need Flask, Celery, and Redis? (with McDonalds in Between)	✓
	Article: Celery: an overview of the architecture and how it works	✓
	Article: Unit Testing Celery Tasks
	Article: Testing Celery Chains
	Article: Task Routing in Celery
	Article: Dynamic Task Routing in Celery
	Article: Dockerize a Celery app with Django and RabbitMQ
	Article: How to call a Celery task from another app
	Article: Distributed Monte Carlo with Celery chords
	Article: An incredibly simple no-frills Celery setup
	Article: 3 Strategies to Customise Celery logging handlers
	Article: Celery task exceptions and automatic retries
	Article: Concurrency and Parallelism	✓
	Article: Celery, docker and the missing startup banner
	Article: Monitoring a Dockerized Celery Cluster with Flower
	Article: Quick Guide: Custom Celery Task Logger
	Article: Celery on Docker: From the Ground up
	Article: Auto-reload Celery on code changes

Databases

Concept	Resource	Done
	Udacity: Intro to relational database	✓
	Udacity: Database Systems Concepts & Design
	Datacamp: Database Design
	Datacamp: Introduction to Databases in Python
	Codecademy: SQL Track	✓
	Datacamp: Intro to SQL for Data Science	✓
	Datacamp: Intermediate SQL
	Datacamp: Querying with TransactSQL	✓
	Datacamp: Joining Data in PostgreSQL
	Udacity: SQL for Data Analysis
	Datacamp: Exploratory Data Analysis in SQL
	Datacamp: Applying SQL to Real-World Problems
	Datacamp: Analyzing Business Data in SQL
	Datacamp: Reporting in SQL
	Datacamp: Data-Driven Decision Making in SQL

Production Environment

Concept	Resource	Done
A/B Testing	Article: Multi-Armed Bandit (MAB) – A/B Testing Sans Regret	✓
	Article: When to Run Bandit Tests Instead of A/B/n Tests	✓
	Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)	✓
	Datacamp: Customer Analytics & A/B Testing in Python
	Udacity: A/B Testing
	Udacity: A/B Testing for Business Analysts
Load Testing	Youtube: Loading Testing with Python	✓
Monitoring	Article: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance	✓
	Article: How to Monitor Models	✓
	Article: The Playbook to Monitor Your Model’s Performance in Production
	Article: Monitoring your Machine Learning Model	✓
	Article: Preventing model drift with continuous monitoring and deployment using Github Actions and Algorithmia Insights	✓
	Article: Continuous monitoring for data projects
	Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production	✓
	Article: Using Statistical Distances for Machine Learning Observability
	Youtube: Instrumentation, Observability & Monitoring of Machine Learning Models
	Article: Incident Management in Machine Learning Systems
	Article: ML Infrastructure Tools — ML Observability
	Youtube: MLOps #24 Monitoring the ML stack // Lina Weichbrodt 0:55:32
	Youtube: Josh Wills: Visibility and Monitoring for Machine Learning Models	✓
	Youtube: Lecture 11B: Monitoring ML Models (Full Stack Deep Learning - Spring 2021) 0:36:55	✓
	Youtube: OpML '20 - How ML Breaks: A Decade of Outages for One Large ML Pipeline
	Youtube: MLOps #28 ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI 0:55:04
	Youtube: MLOps #29 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science 1:00:46
	Youtube: SE4AI: Quality Assessment in Production 1:18:45
	Youtube: SE4AI: Infrastructure Quality, Deployment and Operations 1:04:54

System and Infrastructure Design

Concept	Resource	Done
	Datacamp: Data Engineering for Everyone	✓
	Article: Batch Inference vs Online Inference	✓
	Article: Machine Learning System Design: Real-time processing
	Article: Machine Learning System Design: Models-as-a-service
	Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)	✓
	Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)	✓
	Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)	✓
	Article: The Challenges of Online Inference (Deployment Series: Guide 04)	✓
	Article: Online Inference for ML Deployment (Deployment Series: Guide 05)	✓
	Article: Model Registries for ML Deployment (Deployment Series: Guide 06)	✓
	Youtube: A friendly introduction to System Design
	Youtube: System Design Basics: Horizontal vs. Vertical Scaling
	Youtube: What is a microservice architecture and it's advantages?	✓
	Youtube: Service discovery and heartbeats in micro-services
	Youtube: Avoid cascading failures in a distributed system
	Youtube: How databases scale writes: The power of the log
	Youtube: How to avoid a single point of failure in distributed systems
	Youtube: How to start with distributed systems? Beginner's guide to scaling systems.
	Youtube: What's an Event Driven System?
	Youtube: Why do Databases fail? AntiPatterns to avoid!	✓
	Youtube: What is Consistent Hashing and Where is it used?	✓
	Youtube: What is a Message Queue and Where is it used?	✓
	Youtube: What is an API and how do you design it?
	Youtube: Introduction to NoSQL databases
	Article: Exponential Backoff And Jitter	✓
	Youtube: What is Database Sharding?
	Youtube: What is the Publisher Subscriber Model?
	Article: Shadow mode deployments	✓
	Youtube: Relational database index vs. NoSQL index
	Youtube: Capacity Estimation: How much data does YouTube store daily?
	Youtube: What is Load Balancing?	✓
	Youtube: Distributed Consensus and Data Replication strategies on the server
	Youtube: What is Distributed Caching? Explained with Redis!
	Youtube: Designing Instagram: System Design of News Feed
	Youtube: System Design: Tinder as a microservice architecture
	Youtube: System design : Design Autocomplete or Typeahead Suggestions for Google search
	Youtube: Whatsapp System Design: Chat Messaging Systems for Interviews
	Youtube: How Netflix onboards new content: Video Processing at scale
	Article: Building a feature store	✓
	Article: Model artifacts: the war stories	✓
	Youtube: Feature Stores: An essential part of the ML stack to build great data / Kevin Stumpf - CTO at Tecton 1:05:46
	Youtube: MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali 1:01:35
	Article: How to Deploy a Machine Learning Model	✓
	Article: How to properly ship and deploy your machine learning model	✓
	Article: The Ultimate Guide to Model Retraining
	Youtube: Lecture 11A: Deploying ML Models (Full Stack Deep Learning - Spring 2021) 0:53:25	✓
	Article: Deploying Machine Learning Models: A Checklist	✓
	Article: How to put machine learning models into production	✓
	Article: Key Concepts for Deploying Machine Learning Models to Mobile	✓
	Youtube: MLOps meetup #5 High Stakes ML with Flavio CLesio 0:55:27
	Youtube: MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline 0:56:17
	Youtube: The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43 0:58:31
	Article: How to build scalable Machine Learning systems — Part 1/2	✓
	Article: Machine learning is going real-time	✓
	Book: Machine Learning Systems Design
	Article: ML Infrastructure Tools for Model Building
	Article: ML Infrastructure Tools for Production (Part 1)
	Article: ML Infrastructure Tools for Production
	Article: Data Lineage — An Operational perspective
	Article: Data Pipelines — Agile considerations
	Article: Securing ML applications
	Article: Getting machine learning to production	✓
	Article: Machine Learning to Production	✓
	Youtube: SE4AI: Invited Talk Molham Aref "Business Systems with Machine Learning" 0:47:53
	Youtube: SE4AI: Software Architecture of AI-Enabled Systems 1:14:24
	Youtube: MLOps #31 Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist 0:56:35
	Youtube: MLOps #35: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent 0:52:50
	Youtube: MLOps #15 - Scaling Human in the Loop Machine Learning with Robert Munro 0:55:04
	Youtube: MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey 0:55:42
	Youtube: #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre 0:59:28
	Youtube: MLOps #18 // Nubank - Running a fintech on ML 0:53:19
	Youtube: Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models	✓
	Doc: Lecture 3: Data engineering
	Youtube: MLOps #14: Kubeflow vs MLflow with Byron Allen 0:54:57
	Youtube: Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production 0:47:23
	Stanford MLSys Seminar Episode 1: Marco Tulio Ribeiro 1:00:38
	Stanford MLSys Seminar Episode 2: Matei Zaharia 0:59:44
	Stanford MLSys Seminar Episode 3: Virginia Smith 1:00:55
	Stanford MLSys Seminar Episode 4: Alex Ratner 1:13:34
	Stanford MLSys Seminar Episode 5: Chip Huyen 1:06:44	✓
	Youtube: Xavier Amatriain on Practical Deep Learning Systems (Full Stack Deep Learning - November 2019)

Mathematics

Concept	Resource	Done
Probability	Article: Entropy, Cross Entropy, and KL Divergence	✓
	Article: Interview Guide to Probability Distributions
	Article: Entropy of a probability distribution — in layman’s terms
	Article: KL Divergence — in layman’s terms
	Article: Probability Distributions
	Article: Cross-Entropy and KL Divergence
	Article: Why Randomness Is Information?
	Article: Basic Probability Theory
	Datacamp: Foundations of Probability in Python	✓
Statistics	Datacamp: Introduction to Statistics	✓
	Datacamp: Introduction to Statistics in Python	✓
	Datacamp: Hypothesis Testing in Python	✓
	Datacamp: Statistical Thinking in Python (Part 1)	✓
	Datacamp: Statistical Thinking in Python (Part 2)	✓
	Datacamp: Experimental Design in Python	✓
	Datacamp: Statistical Simulation in Python
	edX: Essential Statistics for Data Analysis using Excel	✓
	StatQuest: Histograms, Clearly Explained 0:03:42
	StatQuest: What is a statistical distribution? 0:05:14
	StatQuest: The Normal Distribution, Clearly Explained!!! 0:05:12
	Statistics Fundamentals: Population Parameters 0:14:31
	Statistics Fundamentals: The Mean, Variance and Standard Deviation 0:14:22
	StatQuest: What is a statistical model? 0:03:45
	StatQuest: Sampling A Distribution 0:03:48
	Hypothesis Testing and The Null Hypothesis 0:14:40	✓
	Alternative Hypotheses: Main Ideas!!! 0:09:49
	p-values: What they are and how to interpret them 0:11:22
	How to calculate p-values 0:25:15
	p-hacking: What it is and how to avoid it! 0:13:44
	Statistical Power, Clearly Explained!!! 0:08:19
	Power Analysis, Clearly Explained!!! 0:16:44
	Covariance and Correlation Part 1: Covariance 0:22:23
	Covariance and Correlation Part 2: Pearson's Correlation 0:19:13
	StatQuest: R-squared explained 0:11:01
	The Central Limit Theorem 0:07:35
	StatQuickie: Standard Deviation vs Standard Error 0:02:52
	StatQuest: The standard error 0:11:43
	StatQuest: Technical and Biological Replicates 0:05:27
	StatQuest - Sample Size and Effective Sample Size, Clearly Explained 0:06:32
	Bar Charts Are Better than Pie Charts 0:01:45
	StatQuest: Boxplots, Clearly Explained 0:02:33
	StatQuest: Logs (logarithms), clearly explained 0:15:37
	StatQuest: Confidence Intervals 0:06:41
	StatQuickie: Thresholds for Significance 0:06:40
	StatQuickie: Which t test to use 0:05:10
	StatQuest: One or Two Tailed P-Values 0:07:05
	The Binomial Distribution and Test, Clearly Explained!!! 0:15:46
	StatQuest: Quantiles and Percentiles, Clearly Explained!!! 0:06:30
	StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained 0:06:55
	StatQuest: Quantile Normalization 0:04:51
	StatQuest: Probability vs Likelihood 0:05:01	✓
	StatQuest: Maximum Likelihood, clearly explained!!! 0:06:12
	Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0 0:09:39
	Why Dividing By N Underestimates the Variance 0:17:14
	Maximum Likelihood for the Binomial Distribution, Clearly Explained!!! 0:11:24
	Maximum Likelihood For the Normal Distribution, step-by-step! 0:19:50
	StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
	StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
	Live 2020-04-20!!! Expected Values 0:33:00
	Udacity: Statistics
	Udacity: Intro to Descriptive Statistics
	Udacity: Intro to Inferential Statistics	✓
Calculus	The Essence of Calculus, Chapter 1 0:17:04
	The paradox of the derivative | Essence of calculus, chapter 2 0:17:57
	Derivative formulas through geometry | Essence of calculus, chapter 3 0:18:43
	Visualizing the chain rule and product rule | Essence of calculus, chapter 4 0:16:52
	What's so special about Euler's number e? | Essence of calculus, chapter 5 0:13:50
	Implicit differentiation, what's going on here? | Essence of calculus, chapter 6 0:15:33
	Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7 0:18:26
	Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8 0:20:46
	What does area have to do with slope? | Essence of calculus, chapter 9 0:12:39
	Higher order derivatives | Essence of calculus, chapter 10 0:05:38
	Taylor series | Essence of calculus, chapter 11 0:22:19
	What they won't teach you in calculus 0:16:22
	But what is a Neural Network? | Deep learning, chapter 1 0:19:13	✓
	Gradient descent, how neural networks learn | Deep learning, chapter 2 0:21:01	✓
	What is backpropagation really doing? | Deep learning, chapter 3 0:13:54	✓
	Backpropagation calculus | Deep learning, chapter 4 0:10:17	✓
	Article: A Visual Tour of Backpropagation	✓
Linear Algebra	Vectors, what even are they? | Essence of linear algebra, chapter 1 0:09:52
	Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2 0:09:59
	Linear transformations and matrices | Essence of linear algebra, chapter 3 0:10:58
	Matrix multiplication as composition | Essence of linear algebra, chapter 4 0:10:03
	Three-dimensional linear transformations | Essence of linear algebra, chapter 5 0:04:46
	The determinant | Essence of linear algebra, chapter 6 0:10:03
	Inverse matrices, column space and null space | Essence of linear algebra, chapter 7 0:12:08
	Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8 0:04:27
	Dot products and duality | Essence of linear algebra, chapter 9 0:14:11
	Cross products | Essence of linear algebra, Chapter 10 0:08:53
	Cross products in the light of linear transformations | Essence of linear algebra chapter 11 0:13:10
	Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12 0:12:12
	Change of basis | Essence of linear algebra, chapter 13 0:12:50
	Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14 0:17:15
	Abstract vector spaces | Essence of linear algebra, chapter 15 0:16:46
	Article: Introduction to Linear Algebra for Applied Machine Learning with Python
	Article: Relearning Matrices as Linear Functions
	Article: You Could Have Come Up With Eigenvectors - Here's How
	Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
	Article: Interactive Visualization of Why Eigenvectors Matter
	Book: Basics of Linear Algebra for Machine Learning
	Computational Linear Algebra for Coders
	1. The Geometry of Linear Equations 0:39:49	✓
	2. Elimination with Matrices. 0:47:41	✓
	3. Multiplication and Inverse Matrices 0:46:48	✓
	4. Factorization into A = LU 0:48:05	✓
	5. Transposes, Permutations, Spaces R^n 0:47:41	✓
	6. Column Space and Nullspace 0:46:01	✓
	9. Independence, Basis, and Dimension 0:50:14	✓
	10. The Four Fundamental Subspaces 0:49:20	✓
	11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55	✓
	14. Orthogonal Vectors and Subspaces 0:49:47	✓
	15. Projections onto Subspaces 0:48:51	✓
	16. Projection Matrices and Least Squares 0:48:05	✓
	17. Orthogonal Matrices and Gram-Schmidt 0:49:09	✓
	21. Eigenvalues and Eigenvectors 0:51:22	✓
	22. Diagonalization and Powers of A 0:51:50
	24. Markov Matrices; Fourier Series 0:51:11
	25. Symmetric Matrices and Positive Definiteness 0:43:52
	27. Positive Definite Matrices and Minima 0:50:40
	29. Singular Value Decomposition 0:40:28
	30. Linear Transformations and Their Matrices 0:49:27
	31. Change of Basis; Image Compression 0:50:13
	33. Left and Right Inverses; Pseudoinverse 0:41:52
	Udacity: Eigenvectors and Eigenvalues	✓
	Udacity: Linear Algebra Refresher

Interview Preparation

Concept	Resource	Done
	Book: Machine Learning Interviews
	Datacamp: Preparing for Statistics Interview Questions in Python	✓
	Datacamp: Practicing Machine Learning Interview Questions in Python	✓
	Datacamp: Kaggle Competition	✓
	Udacity: Optimize your GitHub	✓
	Udacity: Strengthen Your LinkedIn Network & Brand	✓
	Udacity: Data Science Interview Prep	✓
	Udacity: Full-Stack Interview Prep	✓
	Udacity: Refresh Your Resume
	Udacity: Craft Your Cover Letter
	Youtube: Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning	✓
	Youtube: Tutorial: Technical Blogging for Python Programmers	✓

Specialized Skills

Machine Learning Libraries

Concept	Resource	Done
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	✓
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	✓
	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)	✓
Jupyter	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	✓
DVC	Versioning Data with DVC (Hands-On Tutorial!) 0:13:04	✓
	Sharing Data and Models with DVC (Hands-On Data Science Tutorial!) 0:08:53	✓
	Article: ML Ops: Data Science Version Control
	Youtube: Data versioning in machine learning projects - Dmitry Petrov 0:34:44	✓
	Zoom: Data versioning with DVC Part 1	✓
	Zoom: Data versioning with DVC Part 2	✓
scikit-learn	Article: Stacking made easy with Sklearn	✓
	Article: Curve Fitting With Python	✓
	Article: A Guide to Calibration Plots in Python	✓
	Calmcode: human-learn	✓
	Datacamp: Supervised Learning with scikit-learn	✓
	Datacamp: Machine Learning with Tree-Based Models in Python	✓
	Datacamp: Introduction to Linear Modeling in Python
	Datacamp: Linear Classifiers in Python	✓
	Datacamp: Generalized Linear Models in Python
	Notebook: scikit-learn tips	✓
	Pluralsight: Building Machine Learning Models in Python with scikit-learn	✓
	Video: human learn
	Youtube: dabl: Automatic Machine Learning with a Human in the Loop 00:25:43	✓
	Youtube: Multilabel and Multioutput Classification -Machine Learning with TensorFlow & scikit-learn on Python	✓
	Youtube: DABL: Automatic machine learning with a human in the loop- AI Latim American SumMIT Day 1	✓
Tensorflow	Coursera: Introduction to Tensorflow	✓
	Coursera: Convolutional Neural Networks in TensorFlow	✓
	Deeplizard: Keras - Python Deep Learning Neural Network API	✓
	Book: Deep Learning with Python (Page: 276)
	Datacamp: Deep Learning in Python	✓
	Datacamp: Convolutional Neural Networks for Image Processing	✓
	Datacamp: Introduction to TensorFlow in Python	✓
	Datacamp: Introduction to Deep Learning with Keras	✓
	Datacamp: Advanced Deep Learning with Keras	✓
	Pluralsight: Deep Learning with Keras	✓
	Udacity: Intro to TensorFlow for Deep Learning	✓
PyTorch	Article: Keeping Up with PyTorch Lightning and Hydra
	Article: The One PyTorch Trick Which You Should Know
	Article: How does automatic differentiation really work?
	Article: 7 Tips To Maximize PyTorch Performance	✓
	Article: An introduction to PyTorch Lightning with comparisons to PyTorch	✓
	Article: Converting From Keras To PyTorch Lightning	✓
	Article: From PyTorch to PyTorch Lightning — A gentle introduction	✓
	Article: Introducing PyTorch Lightning Sharded: Train SOTA Models, With Half The Memory	✓
	Article: Sharded: A New Technique To Double The Size Of PyTorch Models	✓
	Article: Understanding Bidirectional RNN in PyTorch	✓
	Article: A developer-friendly guide to mixed precision training with PyTorch
	Article: A developer-friendly guide to model pruning in PyTorch
	Article: A developer-friendly guide to model quantization with PyTorch
	Article: Tricks for training PyTorch models to convergence more quickly
	Article: PyTorch Lightning Bolts — From Linear, Logistic Regression on TPUs to pre-trained GANs	✓
	Article: Scaling Logistic Regression Via Multi-GPU/TPU Training	✓
	Article: Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups	✓
	Article: PyTorch Lightning 0.9 — synced BatchNorm, DataModules and final API!	✓
	Article: PyTorch Lightning: Metrics	✓
	Article: PyTorch Multi-GPU Metrics Library and More in PyTorch Lightning 0.8.1	✓
	Article: Distributed model training in PyTorch using DistributedDataParallel
	Article: EINSUM IS ALL YOU NEED - EINSTEIN SUMMATION IN DEEP LEARNING
	Article: Faster Deep Learning Training with PyTorch – a 2021 Guide
	Article: Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
	Article: PyTorch Lightning V1.2.0- DeepSpeed, Pruning, Quantization, SWA
	Article: But what are PyTorch DataLoaders really?
	Article: Using PyTorch + NumPy? You're making a mistake.	✓
	Article: How Wadhwani AI Uses PyTorch To Empower Cotton Farmers	✓
	Article: Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health	✓
	Article: How to Build a Streaming DataLoader with PyTorch
	Article: Transform your ML-model to Pytorch with Hummingbird	✓
	Article: PyTorch Loss Functions: The Ultimate Guide	✓
	Article: Pad pack sequences for Pytorch batch processing with DataLoader	✓
	Article: Model Parallelism
	Notebook: Tensor Arithmetic
	Notebook: Autograd
	Notebook: Optimization
	Notebook: Network modules
	Notebook: Datasets and Dataloaders
	Documentation: Pytorch Lightning	✓
	Datacamp: Introduction to Deep Learning with PyTorch	✓
	Deeplizard: Neural Network Programming - Deep Learning with PyTorch	✓
	Udacity: Intro to Deep Learning with PyTorch
	Youtube: PyTorch Lightning 101	✓
	Youtube: SimCLR with PyTorch Lightning	✓
	Youtube: PyTorch Performance Tuning Guide 26:41:00
	Youtube: Skin Cancer Detection with PyTorch	✓
	Youtube: Learn with Lightning	✓
	Youtube: PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets 00:15:51	✓
	Youtube: Pytorch Zero to All	✓
	PyTorch Developer Day 2020 | Full Livestream	✓
	Youtube: Lightning Chat: How a Grandmaster Won a Kaggle Competition Using Pytorch Lightning	✓
	Youtube: Production Inference Deployment with PyTorch
	Youtube: What is Automatic Differentiation?
BeautifulSoup	Docs: Beautiful Soup Documentation	✓
	Datacamp: Importing Data in Python (Part 2)	✓
	Datacamp: Web Scraping in Python

Docker and Containerization

Concept	Resource	Done
	Pluralsight: Docker and Kubernetes: The Big Picture	✓
	Youtube: Docker	✓
	Youtube: Why Your Web Server Should Log to Stdout (Especially with Docker)	✓
	Article: How To Pass Environment Info During Docker Builds	✓
	Article: Pass Docker Environment Variables During The Image Build	✓
	Article: Setting Default Docker Environment Variables During Image Build	✓
	Article: Docker Explained Visually, For Non-Technical Folks	✓
	Article: Tensorflow in Docker	✓
	Article: Enough Docker to be Dangerous	✓
	Article: How Docker Can Help You Become A More Effective Data Scientist	✓
	Article: Deploying conda environments in (Docker) containers - how to do it right
	Article: Configuring Gunicorn for Docker	✓
	Article: How to scale services using Docker Compose	✓
	Article: A Beginner-Friendly Introduction to Containers, VMs and Docker	✓
	Article: Smaller Docker images with Conda	✓
	Pluralsight: Docker and Containers: The Big Picture	✓
	Article: Docker for Machine Learning – Part I	✓
	Article: Docker for Machine Learning – Part II	✓
	Article: Docker for Machine Learning – Part III	✓
	Article: Using Docker to Generate Machine Learning Predictions in Real Time	✓
	Article: Connection refused? Docker networking and how it impacts your image
	Article: Faster or slower: the basics of Docker build caching
	Article: Where’s your code? Debugging ImportError and ModuleNotFoundErrors in your Docker image
	Article: A tableau of crimes and misfortunes: the ever-useful docker history
	Article: Broken by default: why you should avoid most Dockerfile examples
	Article: A review of the official Dockerfile best practices: good, bad, and insecure
	Article: The best Docker base image for your Python application (February 2021)
	Article: A deep dive into the official Docker image for Python
	Article: Using Alpine can make Python Docker builds 50× slower
	Article: Building on solid ground: ensuring reproducible Docker builds for Python
	Article: Installing system packages in Docker with minimal bloat
	Article: Less capabilities, more security: minimizing privilege escalation in Docker
	Article: Avoiding insecure images from Docker build caching
	Article: Build secrets in Docker and Compose, the secure way
	Article: Security scanners for Python and Docker: from code to dependencies
	Article: The high cost of slow Docker builds
	Article: Faster Docker builds with pipenv, poetry, or pip-tools
	Article: Elegantly activating a virtualenv in a Dockerfile
	Article: Poetry vs. Docker caching: Fight!
	Article: Speed up pip downloads in Docker with BuildKit’s new caching
	Article: Multi-stage builds #1: Smaller images for compiled code
	Article: Multi-stage builds #2: Python specifics—virtualenv, –user, and other methods
	Article: Multi-stage builds #3: Why your build is surprisingly slow, and how to speed it up
	Article: Configuring Gunicorn for Docker
	Article: Activating a Conda environment in your Dockerfile
	Article: Shrink your Conda Docker images with conda-pack
	Article: What’s running in production? Making your Docker images identifiable
	Article: Your Docker build needs a smoke test
	Article: Docker BuildKit: faster builds, new features, and now it’s stable
	Article: Docker vs. Singularity for data processing: UIDs and filesystem access
	Article: Where’s that log file? Debugging failed Docker builds
	Article: An Introduction to Kubernetes for Data Scientists
	Article: How to Use Kubernetes Pods for Machine Learning
	Article: Kubernetes Jobs for Machine Learning
	Article: Kubernetes CronJobs for Machine Learning
	Article: Kubernetes Deployments for Machine Learning
	Article: Kubernetes Services for Machine Learning
	“Let’s use Kubernetes!” Now you have 8 problems
	Article: Kubernetes for Python Developers: Part 1
	Doc: Environment variables in Compose	✓
	Udacity: Scalable Microservices with Kubernetes

Cloud Computing

Concept	Resource	Done
Theory	Datacamp: Cloud Computing for Everyone	✓
AWS	Article: A deep dive into AWS spot instance interruptions	✓
	Pluralsight: AWS Developer: The Big Picture	✓
	Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)	✓
	Pluralsight: AWS VPC Operations	✓
	Pluralsight: Building Applications Using Elastic Beanstalk	✓
	Udemy: AWS Concepts	✓
	Udemy: AWS Certified Developer - Associate 2018	✓
	Whitepaper: Architecting for the Cloud AWS Best Practices	✓
	Whitepaper: AWS Well-Architected Framework	✓
	Whitepaper: AWS Security Best Practices	✓
	Whitepaper: Blue/Green Deployments on AWS	✓
	Whitepaper: Microservices on AWS	✓
	Whitepaper: Optimizing Enterprise Economics with Serverless Architectures	✓
	Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS	✓
	Whitepaper: Running Containerized Microservices on AWS	✓
	Udemy: Serverless Concepts	✓
	Whitepaper: Serverless Architectures with AWS Lambda	✓
	Youtube: Deploying a machine learning model to the cloud using AWS Lambda	✓
	AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription	✓
	AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker	✓
	AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications	✓
	AWS: Hands-on Rekognition: Automated Video Editing	✓
	AWS: Introduction to Amazon Comprehend	✓
	AWS: Introduction to Amazon Comprehend Medical	✓
	AWS: Introduction to Amazon Elastic Inference	✓
	AWS: Introduction to Amazon Forecast	✓
	AWS: Introduction to Amazon Lex	✓
	AWS: Introduction to Amazon Personalize	✓
	AWS: Introduction to Amazon Polly	✓
	AWS: Introduction to Amazon SageMaker Ground Truth	✓
	AWS: Introduction to Amazon SageMaker Neo	✓
	AWS: Introduction to Amazon Transcribe	✓
	AWS: Introduction to Amazon Translate	✓
	AWS: Introduction to AWS Marketplace - Machine Learning Category	✓
	AWS: Machine Learning Exam Basics	✓
	AWS: Neural Machine Translation with Sockeye	✓
	AWS: Process Model: CRISP-DM on the AWS Stack	✓
	AWS: Satellite Image Classification in SageMaker	✓
	Datacamp: Introduction to AWS Boto in Python	✓
	edX: Amazon SageMaker: Simplifying Machine Learning Application Development	✓