Guide · Open source
How to use the MorvanZhou tutorials: a guide for beginners
Updated · written with AI from the project's own documentation and checked against our catalogue data
The MorvanZhou/tutorials repository is the source collection behind the Chinese-language tutorials by Morvan Zhou (莫烦Python). This guide explains what the repository contains, how it relates to the full lessons on the author's website, and how a beginner can start learning from them. By the end you will know where to find each topic, how to get the repository, and what to expect before you begin.
MorvanZhou/tutorialsMorvanZhou/tutorials · ★ 13.1k · MIT Install options & downloads Quick start
- 1Open the homepage. Go to https://morvanzhou.github.io/tutorials to see the list of lessons.
- 2Pick a topic. Choose a section such as Python basics, machine learning or data processing that matches your goal.
- 3Read the lesson online. Open the lesson link and read the Chinese notes, optionally alongside the author's videos.
- 4Clone the repository (optional). Run git clone https://github.com/MorvanZhou/tutorials to keep a local copy of the source files.
- 5Check the date. Remember the last push was December 2020, so confirm framework versions against current documentation before you code.
What is in the MorvanZhou tutorials repository?
The repository is the source collection for tutorials by Morvan Zhou, written in Chinese. Its README mainly points to the full lessons hosted on the author's website rather than containing the lessons themselves.
The topics are grouped into Python basics, machine learning, data processing and other subjects. Machine learning covers reinforcement learning, TensorFlow, PyTorch, Theano, Keras and scikit-learn. Data processing covers NumPy, Pandas and Matplotlib.
- Python basics: basics, threading, multiprocessing and a tkinter window tutorial
- Machine learning: an intro, reinforcement learning, evolutionary algorithms, TensorFlow, PyTorch, Theano, Keras, scikit-learn and a practice section
- Data processing: NumPy & Pandas, Matplotlib and web scraping
- Other: Git and a basic Linux guide
Open the full lessons on the author's website
Because the README links to the complete tutorials, the most direct way to learn is to read them online at the homepage, https://morvanzhou.github.io/tutorials, or on the author's site at mofanpy.com. Pick a topic from the list above and follow its link.
The README also notes that the author records the tutorials as videos, so you can pair the written notes with video if you prefer that style.
Clone the repository to keep a local copy
If you want the repository files on your own computer, clone it with Git. The README gives this command. Cloning is optional; the lessons are read online.
git clone https://github.com/MorvanZhou/tutorialsChoose a learning path that fits your goal
The repository suits a few kinds of readers. Beginners who want a guided introduction to machine learning can start with the intro topic and then move to reinforcement learning or a framework. Students who want a structured path can work through Python basics, then NumPy and Pandas. Developers who need a quick reference to older TensorFlow or Theano examples can jump straight to those sections.
- Beginner in machine learning: start with the intro, then pick one framework
- Student learning Python data work: Python basics, then NumPy & Pandas and Matplotlib
- Quick reference: go directly to the TensorFlow, Theano or Keras topic
Know the limits before you start
The material is in Chinese, so English-speaking readers will need a translation tool or another resource. The last push to the repository was December 2020, and the README does not describe updates, so framework versions in the lessons may be out of date.
The repository is educational content, not a runnable library or application. It has no prerequisites listed, but you will need a Python environment to try the code samples you find in the lessons.
What people use it for
- Learning the basics of Python threading and multiprocessing from Chinese-language lessons
- Getting a guided introduction to reinforcement learning with written notes and video
- Looking up older TensorFlow, Theano or Keras examples as a quick reference
- Working through NumPy, Pandas and Matplotlib for basic data handling
Frequently asked questions
How do I install the MorvanZhou tutorials?
You do not need to install anything to read the lessons, because the method is read-online at https://morvanzhou.github.io/tutorials. If you want the repository files, run git clone https://github.com/MorvanZhou/tutorials. The README lists no prerequisites.
Are the MorvanZhou tutorials free?
The repository is published under the MIT licence, and the lessons are read online. The author also asks readers who find them helpful to share them and to consider a sponsorship, which the README links to but does not require.
Are the tutorials in English?
No. The README and the tutorials are written in Chinese. English-speaking readers will need a translation tool or an English-language resource.
Are the framework examples up to date?
Not necessarily. The last push to the repository was December 2020, and the README does not describe updates. Check current framework documentation before relying on version-specific code.
What are good alternatives to the MorvanZhou tutorials?
The repository's comparable projects are fast.ai, the code repository for Hands-On Machine Learning with Scikit-Learn, and D2L (Dive into Deep Learning). Choose fast.ai or D2L if you want English-language material.
Is this a machine learning library I can install?
No. It is educational content, not software. It points to lessons on TensorFlow, PyTorch, Keras and scikit-learn, but you install those frameworks separately.
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Our verdict
Chinese-speaking beginners who want a guided, partly video-based introduction to Python, machine learning and data handling will find this useful. Readers who need English material, current framework versions or a runnable library should pick a different resource, such as fast.ai, D2L or the Hands-On Machine Learning code repository.
Install options & downloads