Learning Data Mining with R

Transcription

1 Learning Data Mining with R Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free. Course Description Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It can be used for dayto-day data analysis tasks. Data mining is a very broad topic and takes some time to learn. This course will help you to understand the mathematical basics quickly, and then you can directly apply what you ve learned in R. This course covers each and every aspect of data mining in order to prepare you for real-world problems. You ll come to understand the different disciplines in data mining. In every discipline, there exist a variety of different algorithms. At least one algorithm of the various classes of algorithms will be covered to give you a foundation to further apply your knowledge to dive deeper into the different flavors of algorithms. After completing this course, you will be able to solve realworld data mining problems. About The Author Romeo Kienzler is a Chief Data Scientist at the IBM Watson IoT Division. In his role, he is involved in international data mining and data science projects to ensure that clients get the most out of their data. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source

2 projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies. What are the requirements? This course is ideal for data analysts and scientists with a basic knowledge of R libraries who would like to explore R s potential to mine data. What am I going to get from this course? Get to know the basic concepts of R: the data frame and data manipulation Discover the powerful tools at hand for data preparation and data cleansing Visually find patterns in data Work with complex data sets and understand how to process data sets Work with complex data sets and understand how to process data sets Get to know how object-oriented programming is done in R Explore graphs and the statistical measure in graphs Gain insights into the different association types Decide what algorithms actually should be used and what the desired and possible outcomes of the analysis should be Grasp the discipline of classification, the mathematical foundation that will help you understand the bayes theorem and the naïve bayes classifier Delve into various algorithms for classification such as KNN and see how they are applied in R Evaluate k-means, Connectivity, Distribution, and Density based clustering Who is the target audience? Through the course, you will come to understand the different disciplines of data mining using hands-on examples where you actually solve real-world problems in

3 R. For every category of algorithm, an example is explained in detail including test data and R code Learning Path: R Programming Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.course Description Do you want to step into the ever-growing field of data science? Do you wish to equip yourself with one of the most widely used language for data science? Packt s Video Learning Paths are 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. Data is on the rise and it s the need of the hour to process it and make sense out it. Analysts and statisticians need to get this job done. It s an art to tactfully and efficiently process data. But, as it goes an art becomes a reality only with the help of right tools and the knowledge of using these right. So, it is with data science. R is a powerful language that provides with all the tools required to build probabilistic models, perform data science, and build machine learning algorithms. With this Learning Path, you ll be introduced to R Studio and the basics of R. Then, you ll taken through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, and making statistical inferences. Finally, the focus will be on machine learning

4 concepts in depth and applying them in the real world with R. The goal of this course to introduce you to R and have a solid knowledge of machine learning and the R language itself. You ll also solve numerous coding challenges throughout the course. This Learning Path is authored by one of the best in the fields. Selva Prabhakaran Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife. What are the requirements? This is for absolute beginners. No prior knowledge of R is required. What am I going to get from this course? Create and master the manipulation of vectors, lists, dataframes, and matrices Write conditional control structures, and debug and handle errors for efficient error handling Handle dates using lubridate and manipulate strings with stringr package Work with databases without having to write SQL using the dplyr package Work on a full-scale data analysis / data munging project Perform pre-model-building steps Understand the working behind core machine learning algorithms Implement unsupervised learning algorithms Construct nice looking charts with Ggplot2

5 Build R packages from scratch and submit them to CRAN Who is the target audience? If you are looking to start your data science career, or are already familiar with data science, statistics, and machine learning concepts, but want to switch to R, this Video Learning Path will be a great place to start. The Learning Path follows a pragmatic approach where you ll find step-by-step instructions of the functions, tools, and concepts, and the reason you re learning about them. Most of the videos close with coding challenges, putting your newly learned skills into practical use immediately. You ll get hands-on working sessions and detailed explanations. Learning Path: R: Real-World Data Mining With R Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.course Description Packt s Video Learning Paths are 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 Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It is very useful for day-to-day data analysis tasks.

6 Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you ve learned in R. This Learning Path explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. This Learning Path is the complete learning process for datahappy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R s vast set of algorithms. The goal of this Learning Path is to help you understand the basics of data mining with R and then get you working on realworld datasets and projects. This Learning Path is authored by some of the best in their fields. What are the requirements? Requires basic knowledge of R What am I going to get from this course? Get to know the basic concepts of R: the data frame and data manipulation Explore graphs and the statistical measure in graphs Implement various dimension reduction techniques to handle large datasets Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining Work with complex data sets and understand how to process data sets

7 Apply data management steps to handle large datasets Create predictive models in order to build a recommendation engine Who is the target audience? This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage. Linear regression in R for Data Scientists Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free. Course Description Linear regression is the primary workhorse in statistics and data science. Its high degree of flexibility allows it to model very different problems. We will review the theory, and we will concentrate on the R applications using real world data (R is a free statistical software used heavily in the industry and academia). We will understand how to build a real model, how to interpret it, and the computational technical details behind it. The goal is to provide the student the computational knowledge necessary to work in the industry, and do applied research, using lineal modelling techniques. Some basic knowledge in statistics and R is recommended, but not necessary. The course complexity increases as it progresses: we review basic R and statistics concepts, we then transition

8 into the linear model explaining the computational, mathematical and R methods available. We then move into much more advanced models: dealing with multilevel hierarchical models, and we finally concentrate on nonlinear regression. We also leverage several of the latest R packages, and latest research. We focus on typical business situations you will face as a data scientist/statistical analyst, and we provide many of the typical questions you will face interviewing for a job position. The course has lots of code examples, real datasets, quizzes, and video. The video duration is 4 hours, but the user is expected to take at least 5 extra hours working on the examples, data, and code provided. After completing this course, the user is expected to be fully proficient with these techniques in an industry/business context. All code and data available at Github. What are the requirements? Ideally some basic statistics and R, though neither is strictly necessary Some previous experience manipulating Excel files What am I going to get from this course? Model basic and complex real world problem using linear regression Understand when models are performing poorly and correct it Design complex models for hierarchical data How to properly prepare the data for linear regression When linear regression is not sufficient Understand how to interpret the results and translate them to actionable insights Who is the target audience? People pursuing a career in Data Science Statisticians needing more practical/computational experience

9 Data modellers People pursuing a career in practical Machine Learning Logistic Regression Workshop using R Step by Step modeling Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free. Course Description This course is a workshop on logistic regression using R. The course Doesn t have much of theory it is more of execution of R command for the purpose Provides step by step process details Step by step execution Data files for the modeling Excel file containing output of these steps What are the requirements? Theory behind logistic regression theory is not covered in this course Familiarity with basic R syntax What am I going to get from this course? Familiar with Syntax for Step by step logistic regression modeling using R Who is the target audience?

10 R professionals Machine Learning A-Z : Hands- On Python & R In Data Science Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free. Course Description Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science. This course is fun and exciting, but at the same time we dive deep into Machine Learning. It is structured the following way: Part 1 Data Preprocessing Part 2 Regression: Simple Linear Re gression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression Part 3 Classification: Logistic Regression, K-NN, SVM,

11 Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification Part 4 Clustering: K-Means, Hierarchical Clustering Part 5 Association Rule Learning: Apriori, Eclat Part 6 Reinforcement Learning: Upper Confidence Bound, Thompson Sampling Part 7 Natural Language Processing: Bag-of-words model and algorithms for NLP Part 8 Deep Learning: Artificial Neural Networks, Convolutional Neural Networks Part 9 Dimensionality Reduction: PCA, LDA, Kernel PCA, QDA Part 10 Model Selection & Boosting: k-fold Cross Validation, XGBoost Moreover, the course is packed with practical exercises which are based on live examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects. What are the requirements? Just some high school mathematics level What am I going to get from this course? Master Machine Learning on Python & R Have a great intuition of many Machine Learning models Make accurate predictions Make powerful analysis Make robust Machine Learning models Create strong added value to your business Use Machine Learning for personal purpose Handle specific topics like Reinforcement Learning, NLP and Deep Learning Handle advanced techniques like Dimensionality Reduction

12 Know which Machine Learning model to choose for each type of problem Build an army of powerful Machine Learning models and know how to combine them to solve any problem Who is the target audience? Anyone interested in Machine Learning Students who have at least high school knowledge in math and who want to start learning Machine Learning Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. Any people who are not that confortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. Any students in college who want to start a career in Data Science. Any data analysts who want to level up in Machine Learning. Any people who are not satisfied with their job and who want to become a Data Scientist. Any people who want to create added value to their business by using powerful Machine Learning tools Manipulating Time Series Data in R with xts & zoo Manipulating Time Series Data in R with xts & zoo is offered on Datacamp by Jeffrey Ryan, Creator of xts and quantmod. This course contains 56 exercises and 16 videos.

13 Manipulating Time Series Data in R: Case Studies Manipulating Time Series Data in R: Case Studies is offered on Datacamp by Lore Dirick, data scientist at Datacamp. This course contains 51 exercises and 13 videos. Mapping in R Mapping in R is offered on statistics.com by Prof. Chris Brunsdon,Professor national university Ireland.The program has an intensive 4 week period Mastering Data Visualization with R Mastering Data Visualization with R is offered on Pluralsight by Matthew Renze. R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating a wide variety of data visualizations. In this course, you will learn how to answer questions about your data by creating advanced data visualizations with R.