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The awesome-machine-learning repo provides a comprehensive set of curated machine learning resources for practitioners of all skill levels. It centralizes references to free online courses, books, blogs, podcasts, and local meetup groups to help users learn and stay up-to-date in machine learning.
The key functionality of the repo is providing curated lists of high-quality machine learning references. For example, courses.md contains over 100 free and paid online courses from top universities and companies. books.md lists free machine learning books covering both introductory and advanced topics. blogs.md collects notable machine learning blogs, podcasts and newsletters.
To generate some of these curated lists, the repo contains web scraping scripts like pull_R_packages.py. This script demonstrates how to scrape an HTML page, extract information, and reformat it. The script parses the R machine learning package listings to generate Markdown documentation.
Overall, the repo simplifies the process of finding quality machine learning educational content across many mediums. By centralizing and curating these resources, users can more easily get started with and stay current in machine learning. The combination of curated reference lists and scripts to generate documentation makes this a useful resource for any ML practitioner.
This section provides an overview of machine learning blogs and podcasts covered in the
blogs.md file. The file contains several curated lists of online resources for practitioners to stay up-to-date with machine learning news, research, and best practices. It aims to serve as a comprehensive starting point.
The file is organized into sections for different types of online content. It covers a wide range of topics from introductory guides to the latest research. Additional sections like
Data Science/Statistics provide single points of access to different learning resources.
By bringing together top blogs and newsletters all in one place, this file aims to save practitioners time spent searching for high-quality content. It serves as an entry point for staying informed on machine learning advances.
This section provides an overview of free machine learning books available in the
books.md file. The books cover a wide range of topics that are fundamental to understanding machine learning like machine learning, deep learning, natural language processing, information retrieval, neural networks, probability, statistics, linear algebra, and calculus.
Many of the books included have accompanying code examples or are available as interactive online books. Some books are also available as early access versions through Manning Publications. By providing such a comprehensive list of high-quality, free machine learning books in one place, this section acts as a valuable resource for practitioners and students to learn about machine learning from online resources.
books.md file contains listings of machine learning books organized into categories. Each book listing includes the title and a brief description, as well as links to access the book. This file does not contain any code itself, but rather serves as documentation to point users to learning resources.
This section provides an overview of free and paid machine learning courses available online. The
courses.md file contains a comprehensive list of over 100 courses from top universities and companies. Courses are organized by topic such as machine learning, deep learning, reinforcement learning, and more.
For each course, details like the course name, link, description and cost are provided. Some of the most popular courses mentioned include the deep learning specialization from Deeplearning.ai, Stanford's machine learning course, the CS231n convolutional neural networks course from Stanford, and Georgia Tech's machine learning courses available on Udacity. By centralizing information on educational resources, this section makes it easier for users to find courses suitable to their needs, interests, and constraints.
This section provides details on ML conferences and exhibitions. The
events.md file contains a list of professional Machine Learning and Artificial Intelligence events. It references two event resources:
A site that lists upcoming conferences and exhibitions in the fields of AI and ML. It contains a comprehensive list of global events with details on each, including location, date, and registration information.
An events platform for developer-focused virtual events on technical and career topics. In addition to AI/ML topics, it also contains workshops and talks for other coding disciplines.
The file focuses exclusively on aggregating information about relevant events from these two sources. It does not contain any code implementations or algorithms. Programmers and researchers looking to stay up-to-date on the latest professional AI/ML conferences and exhibitions can refer to this section. By linking to event listing platforms, it provides a one-stop resource to browse upcoming opportunities.
This section provides details on local Machine Learning meetup groups listed in the file
meetups.md. This file contains listings for two ML meetup groups - the "Bangalore Machine Learning Meetup (BangML)" group in Bangalore, India and the "AI Brasil" group in São Paulo, Brazil.
The file is formatted using Markdown syntax to organize the meetup group listings into sections for each location. An HTML anchor is used for each section heading to facilitate navigation within the file. The Bangalore section lists the name and Meetup.com URL for the BangML group. Similarly, the São Paulo section lists the name and URL for the AI Brasil group. No complex logic or algorithms are implemented in this file - it solely contains a list of relevant meetup groups formatted with Markdown. Someone reviewing this section would gain an understanding of local ML meetup options in Bangalore and São Paulo by reviewing the curated listings provided.
The scripts in the
scripts directory implement web scraping functionality to parse machine learning resources pages and extract relevant information like package names, links, and descriptions. This extracted data is then formatted in Markdown syntax and written out to files which populate sections of the wiki.
The main script is
…/pull_R_packages.py script scrapes the machine learning package listings from the R Project website. It uses the
pq function from the PyQuery library to parse the HTML of the package page.
pq allows selecting elements from the HTML document and extracting text/attribute values. The script loops through each package item, extracting the package name and link with
pq. It then makes a request to the individual package URL and parses it again with
pq to extract the package description. Finally, it writes the formatted entry with the name, link, and description to a file.
The script implements web scraping by first making a request to the main page URL and parsing with
pq. It then loops through each item, extract the needed values, make any additional requests required, and write the formatted output.