Use over 100 solutions to analyze data and build predictive models
Are you interested in understanding machine learning concepts and building real-time projects with R, but don’t know where to start? Then, this is the perfect course for you!
The aim of machine learning is to uncover hidden patterns, unknown correlations, and find useful information from data. In addition to this, through incorporation with data analysis, machine learning can be used to perform predictive analysis. With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data.
Machine learning has similarities to the human reasoning process. Unlike traditional analysis, the generated model cannot evolve as data is accumulated. Machine learning can learn from the data that is processed and analyzed. In other words, the more data that is processed, the more it can learn.
R, as a dialect of GNU-S, is a powerful statistical language that can be used to manipulate and analyze data. Additionally, R provides many machine learning packages and visualization functions, which enable users to analyze data on the fly. Most importantly, R is open source and free.
Using R greatly simplifies machine learning. All you need to know is how each algorithm can solve your problem, and then you can simply use a written package to quickly generate prediction models on data with a few command lines.
By taking this course, you will gain a detailed and practical knowledge of R and machine learning concepts to build complex machine learning models.
What details do you cover in this course?
We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.
This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.
We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.
We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.
We will discover associated terms and underline frequent patterns from transaction data.
We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.
By the end of this course, we will build our own project in the e-commerce domain.
This course will take you from the very basics of R to creating insightful machine learning models with R.
We have combined the best of the following Packt products:
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Meet your expert instructors:
Yu-Wei, Chiu (David Chiu) is the founder of LargitData a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems.
Dipanjan Sarkar is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development. His areas of specialization includes software engineering, data science, machine learning, and text analytics.
Raghav Bali has a master's degree (gold medalist) in IT from the International Institute of Information Technology, Bangalore. He is an IT engineer at Intel, the world's largest silicon company, where he works on analytics, business intelligence, and application development.
Meet your managing editor:
This course has been planned and designed for you by me, Tanmayee Patil. I'm here to help you be successful every step of the way, and get maximum value out of your course purchase. If you have any questions along the way, you can reach out to me and our author group via the instructor contact feature on Udemy.