- This is example text. After quickly learning the syntax, feel free to delete this text and start creating your roadmap. At any point, press the information buttons in the toolbar (above) to see this example roadmap.
+ Since launching Metacademy, I've had a number of people ask ,
- ## A Two-Minute Overview
+ > What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?
- ### Fundamentals
- Here's some text, you can just write like normal text. You can also make text **bold** or *italics,* which should help you emphasize all the great things you want to say.
+ Excellent question!
+ My answer: **consistently work your way through textbooks**.
- Placing a blank line between two text segments creates a new paragraph.
+ I then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, "Oh, I watch what I eat and consistently exercise." Progress requires consistent discipline, motivation, and an ability to work through challenges on your own. _But you already know this._
+ <img width="300px" class="center-image" src="http://i.imgur.com/RpXJXMQ.jpg" alt="My worn out math stats book.">
- Or you could use a blockquote to say something
- > I could have made money this way, and perhaps amused myself writing code. But I knew that at the end of my career, I would look back on years of building walls to divide people, and feel I had spent my life making the world a worse place. - Richard Stallman
- ### Links
+ **But why textbooks**? Because they're one of the few learning mediums where you'll really own the knowledge. You can take a course, a MOOC, join a reading group, whatever you want. But with textbooks, it's an intimate bond. You'll spill brain-juice on every page; you'll inadvertently memorize the chapter titles, the examples, and the exercises; you'll scribble in the margins and dog-ear commonly referenced areas and look for applications of the topics you learn-- the textbook itself becomes a part of your knowledge (the above image is my nearest textbook). Successful learners don't just _read_ textbooks. Learn to use textbooks in this way, and you can master many subjects -- certainly machine learning.
+ In this brief roadmap, I list a few excellent textbooks for advancing your machine learning knowledge and capabilities. I picked these texts after consulting with fellow graduates students, postdocs, and professors at UC Berkeley -- my own experience played a role as well. This list is purposefully sparse. Having 20 textbooks thrown at you is useless.
- Here is an external link to the [free software foundation](http://www.fsf.org)
-
- Here is an internal link to a metacademy concept that uses the concept's *tag*: [convolutional neural networks](convolutional_nets)
- Here is an internal link to a metacademy concept that uses the concept *title* with a shorthand link syntax: [[convolutional neural nets]]
+ # Neophyte
- Here is an **incorrect/missing** internal link to a metacademy concept that uses the concept *title* with a shorthand link syntax: [[convolutional neural networks]]
+ <a href="http://www.amazon.com/gp/product/111866146X?ie=UTF8&camp=1789&creativeASIN=111866146X&linkCode=xm2&tag=metacademy-20">
+ <img width="200px" class="center-image" src="http://i.imgur.com/HJyPwoo.jpg" alt="Data Smart Textbook Image">
+ Data Smart: Using Data Science to Transform Information into Insight </a>
- **Note**: soon we will allow the use of titles instead of tags and will include an autocomplete type of feature
+ My sister, an artist and writer by trade, asked me how she could understand the basics of data science in a nontrivial way. After reading several introductory and pop books in this area, I recommended Data Smart. My sister was able to work through it, and in fact, the next time I saw her we had a delightful conversation about [[logistic regression]] =).
- ### Lists
+ **Expectations**: You'll understand some common machine learning algorithms at a high-level, and you'll be able to implement some simple algorithms in Excel (and a bit in R if you get through the entire book).
+ **Necessary Background**: basic Excel familiarity -- this book is a great starting point if you don’t have a CS/math-based background. Plus, it's not nearly as dry as a typical textbook.
- And of course, you can use lists:
-
- * here is a list item
- * here is another list item
- * here is an embedded list item
- * back to the outer layer
-
- Or numbered lists
-
- 1. Here's a numbered element
- 84. You can use any number to start a numbered list
- 902. here's an embedded numbered list item
-
- Make sure to place a blank line before your lists, other you'll end up with a list that looks like:
- * oops
- * this is wrong
- * so, so wrong
-
- ### Code
- Place inline code between backticks such as `printf()` or specify block-code via
-
- ```
- import sys
- print sys.copyright
- ```
-
- We currently do not have code highlighting/coloring in place, but let us know if you want to use it and we can oblige: [send us feedback](/feedback)
+ _Key Chapters_: It's a short read, and every chapter is fairly illumination -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.
- ### Math
- Math support is possible via [MathJax](http://www.mathjax.org/) by using two dollar signs: $$x^n + y^n \neq z^n \forall n > 2$$
- ### Tables
- First Header | Second Header
- ------------- | -------------
- Content Cell | Content Cell
- Content Cell | Content Cell
- ### Multimedia
- We currently do not support embedded images or videos, but let us know if you want this functionality: [send us feedback](/feedback)
+ # Apprentice
+
+ <a href="http://amzn.to/1kIbPTL">
+ <img width="200px" class="center-image" src="http://i.imgur.com/6TpOD4N.jpg" alt="Machine Learning with R Textbook Image">
+ Machine Learning with R </a>
+
+ This is an example-laden book for simultaneously learning practical machine learning techniques and the R programming language. I read this book after putting R on the shelf for 3 years (for Matlab =\), and after finishing the first few chapters (and remembering that R packages are so damn simple), I haven't gone back to Matlab.
+
+
+ **Expectations**: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R
+
+ **Necessary Background**: No real prerequisites, though the following will help (these can be learned/reviewed as you go):
+
+ * some programming experience [in R]
+ * some algebra
+ * basic calculus
+ * a little bit of probability theory
+
+ **Key Chapters**: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R). If you're looking to shave off some time, you can safely skip chapters 8 and 12.
+
+
+ # Journeyman
+
+ <a href="http://amzn.to/UjGhfq">
+ <img width="200px" class="center-image" src="http://i.imgur.com/yppYN4K.jpg" alt="PRML">
+ Pattern Recognition and Machine Learning </a>
+
+ This stage separates those with a surface-level understanding from those with rigorous, in-depth, knowledge. It starts getting mathy at this stage, but if you plan on making machine learning a substantial part of your career, you'll have to cross this bridge.
+
+ ** Expectations ** Be able to recognize, implement, debug, and interpret the output of most off-the-shelf machine learning methods. Also, you should have an intuition about which advanced ML concepts to investigate for a given problem. Practicing data scientists should at least be at this level.
+
+ **Necessary Background**:
+
+ * you should be comfortable with off-the-shelf clustering and classification algorithms
+ * linear algebra: understand matrix algebra and determinants
+ * some multivariate and vector calculus experience -- know what a Jacobian is
+ * some machine learning implementation experience in R, Matlab, the SciPy stack, or Julia.
+
+ **Key Chapters**: Know and love chapters 1-12.1. Chapters 12.2 - 14 can be consulted as you need them.
+
+ **Note** PRML spends quite a bit of time on Bayesian machine learning methods. If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of [Doing Bayesian Data Analysis](http://amzn.to/1nqV6Kf)
+
+ # Master
+
+ <a href="http://amzn.to/1nWMyK7">
+ <img width="200px" class="center-image" src="http://i.imgur.com/6eD76vT.jpg" alt="PGM">
+ Probabilistic Graphical Models: Principles and Techniques</a>
+
+ There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology. But if you're at this level, you probably have a good sense of what areas you'd like to improve, so I'll stick with the single book recommendation. <a href="http://amzn.to/1nWMyK7">
+ Probabilistic Graphical Models: Principles and Techniques </a> is a classic, monstrous tomb that should be within arms length of any ML researcher worth his/her salt =). PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.
+
+
+ **Expectations**: You'll be able to construct probabilistic models of novel problems, determine a reasonable inference technique, and evaluate your methodology.
+
+ **Necessary Background**:
+
+ * you should be comfortable with most off-the-shelf ML algorithms
+ * linear algebra -- know how to interpret eigenvalues
+ * multivariate and vector calculus experience
+ * some machine learning implementation experience in R, Matlab, the SciPy stack, or Julia.
+
+ **Key Chapters**: Chapters 1-8 cover similar content as Bishop's Pattern Recognition and Machine learning Ch. 2 and 8, but at a much deeper level. Chapters 9-13 contain key content, and Ch. 19 on partially observed data is really helpful. Read Ch. 14 and Ch. 15 when/if they are relevant to your goals.
+
+
+ # Grandmaster
+
+ If you've achieved master status, you'll have a strong enough ML background to pursue any ML-related specialization at a novel level: e.g. maybe you're interested in pursuing novel deep learning applications or characterizations?