Gain a better grasp of the foundational knowledge in statistics and machine learning, understand how to use common R libraries for data processing, model training and web application development.
Key Features
- Well-illustrated theory and codes, making the material intuitive and beginner-friendly
- Essential knowhow with proper context on the usage and application beyond simple knowledge sharing
- Both hard skills for statistics and machine learning and soft presentational skills
Book Description
This workshop title combines thorough explanations of essential concepts, practical examples, and self-assessment questions with detailed hands-on exercises to help you explore the essential topics in statistics and machine learning.
You will learn the critical components of the entire model development process, as well as common applications. You will also build interactive applications that support effective presentation and cover advanced topics such as computer vision and natural language processing.
You'll see how to use R to work with different data types, and how to overcome all kinds of mathematical challenges in R, including algebra, calculus, and probability questions. You'll even see how to use R for linear regression and Bayesian statistical problems.
By the end of this book, you will have a better grasp of the foundational knowledge in statistics and machine learning, understand how to use common R libraries for data processing, model training and web application development, and know how to tweak the inner workings of libraries.
What you will learn
- Understand the theory and practice of essential concepts in statistics and ML
- Learn Algorithms and implementation for popular machine learning approaches
- Learn how to build web applications from scratch
- Lear to create efficient model development and interactive analysis
- Learn how to visualize data with ggplot
- Use R to meet your statistical needs, including Bayesian and linear regression
Who This Book Is For
Beginner to intermediate level data scientists will get a lot out of this book; so will undergraduate to masters-level students, and early to mid-senior data scientist or analytics related roles.
Basic knowledge of linear algebra and modelling will be helpful to understand the concepts covered in this book.
Table of Contents
- Getting started with R
- Data processing with dplyr
- Intermediate Data Processing
- Data visualization with ggplot2
- Exploratory Data analysis
- Effective reporting with R Markdown
- Linear algebra in R
- Intermediate linear algebra in R
- Calculus in R
- Probability basics
- Sampling distribution
- Statistics estimation
- Hypothesis testing
- Linear regression
- Bayesian statistics
