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You can link directly to a section in a rendered file by hovering over the section heading to expose. For more information, see ' Autolinked references and URLs.' Section links Tip: GitHub automatically creates links when valid URLs are written in a comment. Here are the currently supported color models. The background color is `#ffffff` for light mode and `#000000` for dark mode.

A supported color model within backticks will display a visualization of the color. In issues, pull requests, and discussions, you can call out colors within a sentence by using backticks. For more information, see ' About writing and formatting on GitHub.' Supported color models

#README MARKDOWN CODE#

If you are frequently editing code snippets and tables, you may benefit from enabling a fixed-width font in all comment fields on GitHub. Some basic Git commands are:įor more information, see ' Creating and highlighting code blocks.' To format code or text into its own distinct block, use triple backticks.

Use `git status` to list all new or modified files that haven't yet been committed. You can also press the Command+ E (Mac) or Ctrl+ E (Windows/Linux) keyboard shortcut to insert the backticks for a code block within a line of Markdown. The text within the backticks will not be formatted. You can call out code or a command within a sentence with single backticks. For more information about keyboard shortcuts, see ' Keyboard shortcuts.' Quoting code You can quote an entire comment by clicking, then Quote reply. Tip: When viewing a conversation, you can automatically quote text in a comment by highlighting the text, then typing R. Quoted text is indented, with a different type color. StyleĬommand+ B (Mac) or Ctrl+ B (Windows/Linux)Ĭommand+ I (Mac) or Ctrl+ I (Windows/Linux) You can indicate emphasis with bold, italic, strikethrough, subscript, or superscript text in comment fields and. Each heading title is listed in the table of contents and you can click a title to navigate to the selected section. When you use two or more headings, GitHub automatically generates a table of contents that you can access by clicking within the file header. # A first-level heading # A second-level heading # A third-level heading The number of # you use will determine the hierarchy level and typeface size of the heading. This 3-course Specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.To create a heading, add one to six # symbols before your heading text.

#README MARKDOWN HOW TO#

This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)īy the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.In the first course of the Machine Learning Specialization, you will:

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