regression


Top 100 Machine learning Interview Questions and Answers Useful Tips

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Machine learning relates to the study, design, and development of the algorithms that give computers the capability to learn without being explicitly programmed. While data mining can be defined as the process by which the unstructured data tries to extract knowledge or unknown interesting patterns. During this processing machine, learning algorithms are used. Converting a (usually continuous) feature into multiple binary features called buckets or bins, typically based on value range. For example, instead of representing temperature as a single continuous floating-point feature, you could chop ranges of temperatures into discrete bins.


Classification and Regression Trees

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Learn about CART in this guest post by Jillur Quddus, a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud. Although both linear regression models allow and logistic regression models allow us to predict a categorical outcome, both of these models assume a linear relationship between variables. Classification and Regression Trees (CART) overcome this problem by generating Decision Trees. These decision trees can then be traversed to come to a final decision, where the outcome can either be numerical (regression trees) or categorical (classification trees). When traversing decision trees, start at the top.


Machine learning fundamentals: What cybersecurity professionals need to know - Help Net Security

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In this Help Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know. Hi, this is Chris Morales and I'm Head of Security Analytics at Vectra, and in this Help Net Security podcast I want to talk about machine learning fundamentals that I think we all need to know as cybersecurity professionals. AI has become very used within our industry more and more, and here at Vectra we are an AI company as well. As you start to hear more about AI, you have to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just kind of a quick understanding of the different algorithms or understanding when you need to use certain algorithms for specific jobs. There has always been a very muddled use of the terms artificial intelligence, data science and machine learning.


4 Machine Learning Techniques with Python – Rinu Gour – Medium

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While this tutorial is dedicated to Machine Learning techniques with Python, we will move over to algorithms pretty soon. But before we can begin focussing on techniques and algorithms, let's find out if they're the same thing. A technique is a way of solving a problem. This is quite generic as a term. But when we say we have an algorithm, we mean we have an input and we desire a certain output from it.


Beginning with Machine Learning - Part 1

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This question pops into almost everyone's head who so ever wants to play with this new technology. I myself wondered as to from where should I begin, what should I cover and how can I learn quickly! I am not here to give you a list of articles from where you can read or explore. But I will help you through it. To have a basic understanding of almost every important concept so that you can dig into that as well.


matloff/polyreg

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Other than the various cross-validation functions, the main functions are polyfit() and predict.polyFit(). One can fit either regression or classification models, with an option to perform PCA for dimension reduction on the predictors/features. Built in to the latest version of the regtools package. In the former case, getPE() reads in the dataset and does some preprocessing, producing a data frame pe. Forward stepwise regression is also available with FSR which also accepts polynomial degree and interaction as inputs.


Machine Learning - Linear and Logistic Regression -

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I recently took Andrew Ng's Machine Learning course on Coursera, and I'm hoping to write a series of blog posts on what I learnt. In these we will look at a variety of machine learning techniques and categories, starting with linear and logistic regression. Machine learning is something of a buzzword at the moment, but underneath all the hype it's a technology that's expected to revolutionise virtually all industries, and have a huge impact on people's lives in the coming decades. Machine learning problems can be split into supervised learning and unsupervised learning. Supervised learning works by giving the algorithm the "right answers", which are used to train the algorithm so that it can fit and predict when given new examples.


Stock Prediction with ML: Ensemble Modeling -- The Alpha Scientist

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Markets are, in my view, mostly random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small edges together, the rewards can be great. In this article, I'll present a framework for blending together outputs from multiple models using a type of ensemble modeling known as stacked generalization.


Stock Prediction with ML: Ensemble Modeling -- The Alpha Scientist

#artificialintelligence

Markets are, in my view, mostly random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small edges together, the rewards can be great. In this article, I'll present a framework for blending together outputs from multiple models using a type of ensemble modeling known as stacked generalization.


Want to become a Machine Learning Engineer, Grab these 5 Skills which are Must

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Let's understand what machine learning is? In simple words, machine learning is all about making computers to perform intelligent tasks without explicitly coding. This is achieved by training the computer with lots and lots of data. For example, detecting whether a mail is a spam or not recognizing handwritten digit, fraud detection in transactions and many such applications. Now let's see what are the top five skills to get a machine learning job?