amitness/learning

★ 6,866Audience · developerComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((learning))
    Topics covered
      Machine learning
      Software engineering
      NLP and LLMs
      System design
    Resource types
      Online courses
      Books
      Articles
    Platforms
      Coursera fast.ai
      MIT OCW Datacamp
    Audience
      Self-taught engineers
      ML learners

mindmap root((learning)) Topics covered Machine learning Software engineering NLP and LLMs System design Resource types Online courses Books Articles Platforms Coursera fast.ai MIT OCW Datacamp Audience Self-taught engineers ML learners

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Things people build with this

USE CASE 1

Use the log as a curated reading list to discover high-quality ML and software engineering courses and books.

USE CASE 2

Find resources for specific topics like system design, NLP, large language models, or generative AI.

USE CASE 3

Track your own learning progress by forking this format to create your personal study log.

USE CASE 4

Discover which Coursera, fast.ai, or MIT OpenCourseWare resources an experienced engineer found valuable.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a personal learning log maintained by a software engineer who tracks every course, book, and article they work through to build skills in machine learning and software engineering. It is not a software tool or library. The content is a single large README organized into tables, with each row showing a resource and a progress indicator marking whether that item is complete or still in progress. The topics covered span a wide range. On the software fundamentals side, the log includes courses on system design, databases, algorithms, Linux, version control, and UI basics. On the machine learning side, it covers statistics, linear algebra, Python data libraries, deep learning frameworks, natural language processing, large language models, and generative AI. Resources come from platforms like Datacamp, Udacity, Coursera, fast.ai, and MIT OpenCourseWare, as well as books from O'Reilly and Manning. The current focus at the time the README was last updated is Generative AI. The log is updated roughly once a month, making it a slow-moving but long-running record of self-directed study. The author describes the intent as building strong core software engineering skills while also expanding into adjacent areas over time. For someone browsing this repository, it functions as a curated reading list rather than a reference implementation or tutorial. A reader could use it as inspiration for their own learning path, especially in machine learning and AI. There is no code to run, no installation required, and no outputs to review. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Based on amitness/learning, recommend a 6-month self-study plan for a software engineer wanting to move into machine learning.

Prompt 2

List all the NLP and large language model resources in the amitness learning log and suggest the best order to study them.

Prompt 3

What generative AI courses and books does the amitness learning log include, and which should I start with first?

Prompt 4

Help me create a personal learning log in the same markdown table format to track my own courses and progress.

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