What is machine_learning_examples?

lazyprogrammer/machine_learning_examples — explained in plain English

Analysis updated 2026-06-24

★ 8,866PythonAudience · developerComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((ML Examples))
    Topics
      Deep learning
      NLP
      Reinforcement learning
      Computer vision
    Structure
      Course folders
      Colab notebooks
    Audience
      Course students
      Self-learners
    Use Cases
      Follow course code
      Study AI examples

mindmap root((ML Examples)) Topics Deep learning NLP Reinforcement learning Computer vision Structure Course folders Colab notebooks Audience Course students Self-learners Use Cases Follow course code Study AI examples

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What do people build with it?

USE CASE 1

Follow along with deep learning or NLP course videos using the matching Python code examples in this repo.

USE CASE 2

Study reinforcement learning, computer vision, or time series AI with working code samples tied to course lectures.

USE CASE 3

Stay current with course updates by cloning the repo and running git pull rather than forking it.

What is it built with?

PythonTensorFlowGoogle Colab

How does it compare?

	lazyprogrammer/machine_learning_examples	apenwarr/sshuttle	olafenwamoses/imageai
Stars	8,866	8,868	8,870
Language	Python	Python	Python
Setup difficulty	easy	easy	moderate
Complexity	2/5	2/5	2/5
Audience	developer	developer	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

This is a supplement to paid courses at deeplearningcourses.com, code alone without the video lectures is hard to follow.

License not mentioned in the explanation.

So what is it?

This repository is a companion code collection for a series of machine learning and data science courses sold at deeplearningcourses.com by the author who goes by "Lazy Programmer". It contains Python code examples and tutorials that accompany video course content on topics including deep learning, natural language processing, reinforcement learning, computer vision, time series analysis, and financial applications of AI. The repository is organized so that each folder corresponds to one course. Students enrolled in a particular course find the associated code by identifying the folder name, which is explained in an early lecture within each course. The author notes that not all courses have code here: newer courses (particularly those built around TensorFlow 2.0) were taught using Google Colab notebooks, with links provided inside the course lectures rather than in this repository. The README recommends cloning the repository rather than forking it, because the author updates the code frequently and forks quickly fall out of date. Cloning allows students to pull the latest changes easily with a single command. This is not a standalone learning resource. It is specifically a code supplement to paid courses and does not include explanations or lesson text on its own. Someone looking to learn from it without the accompanying course would need to navigate the folder structure based on prior knowledge of what the courses cover.

Copy-paste prompts

Prompt 1

I am enrolled in a Lazy Programmer machine learning course. Show me how to clone this repo and find the right folder for the NLP course.

Prompt 2

How do I run the Python examples from a specific course folder in machine_learning_examples? Walk me through installing the dependencies and running the first script.

Prompt 3

Which folders in the lazyprogrammer/machine_learning_examples repo correspond to deep learning versus reinforcement learning courses?

Prompt 4

Why are some Lazy Programmer courses not represented in this repo, and where do I find the code for TensorFlow 2.0 courses instead?

Frequently asked questions