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Explore the advanced topics in Machine Learning with R in a step by step manner to build powerful predictive models in R
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R: Machine Learning with R – Beginner to Expert!: 4-in-1
Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can learn from and make predictions on data. R language is widely used among statisticians and data miners to develop statistical software and perform data analysis. It provides a cutting-edge power you need to work with Machine Learning techniques. This comprehensive 4-in-1 is a step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language. Apply R to simple predictive modeling with short and simple code. Dive into the advanced algorithms such as hyper-parameter tuning and DeepLearning, and putting your models into production! By the end of this course, you’ll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R!Contents and OverviewThis training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Getting Started with Machine Learning in R, covers learning Machine learning techniques in the popular statistical language R. The course will take you through some different types of ML. Youll work with a classic dataset using Machine Learning. You will learn Linear and Logistic Regression algorithms and analyze the dataset. Youll explore algorithms like Random Forest and Naive Bayes for working on your data in R. Analysis of the data set is demonstrated from end to end, with example R code you can use. Then youll have a chance to do it yourself on another data set. By the end of the course you will learn how to gain insights from complex data and how to choose the correct algorithm for your specific needs. The second course, Advanced Machine Learning with R, covers advanced techniques like hyper parameter tuning, deep learning in a step by step manner with examples. In this course, youll get to know the advanced techniques for Machine Learning with R, such as hyper-parameter turning, deep learning, and putting your models into production through solid, real-world examples. In the first example, youll learn all about neural networks through an example of DNA classification data. Youll explore networks, implement them, and classify them. After that, youll see how to tune hyper-parameters using a data set of sonar data and youll get to know their properties. Next, youll understand unsupervised learning with an example of clustering politicians, where youll explore new patterns, understand unsupervised learning, and visualize and cluster the data. The third course, R Machine Learning solutions, covers building powerful predictive models in R. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. Youll then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. The fourth course, Applied Machine Learning and Deep Learning with R covers building powerful machine learning and deep learning applications with help of the R programming language and its various packages. In this course, youll examine in detail the R software, which is the most popular statistical programming language of recent years. Explore different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, youll dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, youll learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR. By the end of this course, you’ll explore the advanced topics in machine learning with R in a step by step manner with examples to build powerful predictive models in R.About the AuthorsPhil Rennertis a Principal Research Engineer in Information Science, in the overall business of extracting wisdom from information overload. He has a long track record of solving challenging technical problems, innovating new techniques where existing ones don’t apply. He is extensively skilled in machine learning, natural language processing, and data mining. Tim Hoolihancurrently 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, a regional consulting company in the US Midwest. He is the o
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