Start Here
Get Started and Get Good at Applied Machine Learning Hi, Jason here. I’m the guy behind Machine Learning Mastery. My goal is to help you get started, make progress and kick butt with machine learning. I teach a top-down and results-first approach designed for developers and engineers. This is unlike most academic textbooks and university courses. Access my best free tutorials on the blog or take the next step with my paid training material. You may be feeling overwhelmed. Take your time. Table of Contents What do you need help with? How Do I Get Started? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: For more on this top-down approach, see: Many of my students have used this approach to go on and do well in Kaggle competitions and get jobs as Machine Learning Engineers and Data Scientists. Applied Machine Learning Process For a good summary of this process, see the posts: R Machine Learning
Machine Learning - DZone - Refcardz
From a probabilistic viewpoint, the predictive problem can be viewed as a conditional probability estimation; trying to find Y where P(Y | X) is maximized. From the Bayesian rule, P(Y | X) == P(X | Y) * P(Y) / P(X) This is equivalent to finding Y where P(X | Y) * P(Y) is maximized. Let's say the input X contains 3 categorical features— X1, X2, X3. In the general case, we assume each variable can potentially influence any other variable. P(X | Y) == P(X1 | Y) * P(X2 | Y) * P(X3 | Y), we need to find the Y that maximizes P(X1 | Y) * P(X2 | Y) * P(X3 | Y) * P(Y) Each term on the right hand side can be learned by counting the training data. But it is possible that some patterns never show up in training data, e.g., P(X1=a | Y=y) is 0. P(X1=a | Y=y) == (count(a, y) + 1) / (count(y) + m) …where m is the number of possible values in X1. When the input features are numeric, say a = 2.75, we can assume X1 is the normal distribution. Here is how we use Naïve Bayes in R:
ConvNetJS: Deep Learning in your browser
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. Description The library allows you to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (I am a PhD student at Stanford). Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2) cost functions Ability to specify and train Convolutional Networks that process images An experimental Reinforcement Learning module, based on Deep Q Learning. Head over to Getting Started for a tutorial that lets you get up and running quickly, and discuss Documentation for all specifics. Code The code is available on Github under MIT license and I warmly welcome pull requests for new features / layers / demos and miscellaneous improvements. Discussion Group
Center for Machine Learning and Intelligent Systems | University of California, Irvine
Introduction to Strategic Thinking
Starts June 22, 2015 This is a short interdisciplinary course on strategic thinking and some of its most powerful tools. Strategic thinking is not exclusive to business or military applications. We will draw lessons and use concepts from economics, game theory, scenario planning, behavioral sciences, and futures studies, as well as philosophy and linguistics. This course is designed to be a starting point for people who are just beginning to learn about strategic thinking. "Fascinating course. "The course was very enlightening! "Thank you for the stimulus and providing a completely different approach from what I was expecting from this class." "I come from a background where I learned, used and taught military doctrine and decision making and I therefore confidently entered into this course expecting to have my expertise confirmed and (possibly!) Sandjar Kozubaev Economist & Strategist Sandjar Kozubaev is an economist and a strategist.
7 Machine Learning Algorithms You Should Know Of : Tech : University Herald
A machine learning algorithm is used in many ways to identify incorrect or correct data that is fed into the system. It is first given some sort of a "teaching set" of data, which is then used to answer a question. As more and more questions are asked, this new information is added to the algorithm making it smarter and better at performing its task over time. So one can say that these machines are "learning." Here are seven of the most common uses of this technology. Financial Trading Advertisement A lot of people want to find out what will happen to the stock market ahead of time. Data Security Malware is becoming a huge threat to data security. Medicine Machine learning algorithms can be used to detect risk factors for various diseases even before their human counterparts do. Fraud Detection Paypal, for example, uses a machine algorithm to prevent money laundering. Online Search Have you noticed how Google can be so accurate when it suggests words before you even complete typing them.
Simple machine learning: bot detection
An Introduction to Feature Selection
Which features should you use to create a predictive model? This is a difficult question that may require deep knowledge of the problem domain. It is possible to automatically select those features in your data that are most useful or most relevant for the problem you are working on. This is a process called feature selection. In this post you will discover feature selection, the types of methods that you can use and a handy checklist that you can follow the next time that you need to select features for a machine learning model. An Introduction to Feature SelectionPhoto by John Tann, some rights reserved What is Feature Selection Feature selection is also called variable selection or attribute selection. It is the automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on. feature selection… is the process of selecting a subset of relevant features for use in model construction Filter Methods
Machine Learning for Programmers
Leap From Developer To Machine Learning Practitioner or, my answer to the question: How Do I Get Started In Machine Learning? I’m a developer. I have read a book or some posts on machine learning. Does this sound familiar? Frustrated with machine learning books and courses? The most common question I’m asked by developers on my newsletter is: How do I get started in machine learning? I honestly cannot remember how many times I have answered it. In this post, I lay out all of my very best thinking on this topic. You will discover why the traditional approach to teaching machine learning does not work for you.You will discover how to flip the entire model on its head.And you will discover my simple but very effective antidote that you can use to get started. Let’s get into it… A Developer Interested in Machine Learning You are a developer and you’re interested in getting into machine learning. You read some blog posts. Sound familiar? You try some video courses. Machine Learning Engineer 1. 2. 3.
