Learning Path: R: Complete Guide to Machine Learning with R | Data Science Online Course by Udemy

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Get acquainted with the various stages of machine learning using the R language

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Learning Path: R: Complete Guide to Machine Learning with R

Machine Learning is a growing field that focuses on teaching computers to do work that was traditionally reserved for humans. It is a cross-functional domain that uses concepts from statistics, math, software engineering, and more. R language is widely used among statisticians and data miners to develop statistical software and perform data analysis. So, if you’re looking at mastering the techniques of machine learning, then go for this Learning Path. Packts Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Organize and set up your data and make predictions Explore important machine learning techniques such as linear and logistic regression, data scaling, cross-validation, neural network, hyper parameter tuning, and unsupervised learning Work with a variety of real-world algorithms that suit your problem Let’s take a quick look at your learning journey. This Learning Path will start by organizing your data and then predicting it. You will then go through an example on how to predict murder arrest rate based on arrest data for a given state. Youll explore different R libraries and learn how to work with RStudio. Youll then learn to apply linear regression, calculate scores for test sets, and plot test results on a Cartesian plane. Youll also learn to use logistic regression for predicting a classification problem on automobile data. Next, youll go through the advanced techniques of machine learning with R, such as hyper-parameter tuning, deep learning, and putting your models into production through solid, real-world examples. Finally, youll understand unsupervised learning with an example of clustering politicians, where youll explore new patterns and visualize and cluster the data. By the end of this Learning Path, youll have a solid understanding of machine learning and be able to make predictions on your data in no time! Meet Your Expert: We have the best works of the following esteemed authors to ensure that your learning journey is smooth: Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the senior director of data science there. Prior to that, he was CTO at level seven in a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group. In his job, he uses deep neural networks to help automate of lot of conversation classification problems. In addition, he works on some side-projects researching other areas of artificial intelligence and machine learning. Outside data science, he is interested in mathematical computation in general; he is a lifelong math learner and really enjoys applying it wherever he can. He also knows a variety of languages including R, Python, Ruby, PHP, C/C++, and many more. Dr. Samik Sen is a theoretical physicist and loves thinking about hard problems. After his PH.D, in developing computational methods to solve problems for which no solutions existed, he began thinking about how to tackle math problems while lecturing. He developed algorithms to generate problem sets and solutions and learned how to create video lessons. He has since developed a large Facebook community teaching school math around Ireland, with associated e-learning products and a YouTube channel. Samik is currently fascinated by machine- and deep-learning, which seem to be world-changing on the scale of Calculus. He has been developing a machine learning system to do this, which has begun doing better than he could himself (this was his original intention), spotting structures he’d never heard of. He has a YouTube channel associated with data science, which also provides a valuable engagement with people round the world who look at problems from a different perspective.

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