Unleash the true potential of R to unlock the hidden layers of data
Are you looking to gain in-depth knowledge of machine learning and deep learning? If yes, then this Learning Path just right for you.
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.
R is one of the leading technologies in the field of data science. Starting out at a basic level, this Learning Path will teach you how to develop and implement machine learning and deep learning algorithms using R in real-world scenarios.
The Learning Path begins with covering some basic concepts of R to refresh your knowledge of R before we deep-dive into the advanced techniques. You will start with setting up the environment and then perform data ETL in R. You will then learn important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Next, you will understand the basics of deep learning and artificial neural networks and then move on to exploring topics such as ANNs, RNNs, and CNNs. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering.
By the end of the Learning Path, you will have a solid knowledge of all these algorithms and techniques and be able to implement them efficiently in your data science projects.
Do not worry if this seems too far-fetched right now; we have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:
- 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.
- 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. In addition to being a startup entrepreneur and data scientist, he specializes in using Spark and Hadoop to process Big Data and apply data mining techniques for data analysis.
- Vincenzo Lomonaco is a deep learning PhD student at the University of Bologna and founder of ContinuousAI, an open source project aiming to connect people and reorganize resources in the context of continuous learning and AI. He is also the PhD students' representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses machine learning and computer architectures in the same department.
- Basic knowledge of R would be beneficial
- Knowledge of linear algebra and statistics is required
- Develop R packages and extend the functionality of your model
- Perform pre-model building steps
- Understand the working behind core machine learning algorithms
- Build recommendation engines using multiple algorithms
- Incorporate R and Hadoop to solve machine learning problems on Big Data
- Understand advanced strategies that help speed up your R code
- Learn the basics of deep learning and artificial neural networks
- Learn the intermediate and advanced concepts of artificial and recurrent neural networks
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