What you would learn in Applied Data Science and Machine Learning in R for Beginners course?
Through this class, we've taught the fundamentals of data science methods and machine learning techniques, starting with the basics of R programming. After the program, any beginner interested in data research and machine learning can sail through the course on their own. This means students can develop the expertise they need by absorbing the knowledge they have learned through this course.
We will not just apply the functions predefined in R and various packages but also learn to construct machine learning algorithms by defining the functions we want.
In this class, we'll be covering topics such as programming in R, the basics of machine learning and data science understanding of data preprocessing data, the cleansing of data, and the way to use tools from data science to analyze and preprocess data (structured as and unstructured) and create predictive models, clustering, PCA and so on.
The topics of machine learning would include supervised and unsupervised learning. In supervised learning, we'll discuss topics like logistic regression, linear regression, forecasting, time series, analysis of the text (part of the natural processing of languages), and neural network support vector machine marketing basket analyses (association regulations).
As part of unsupervised learning We will be examining clustering techniques, including non-hierarchical and hierarchical clustering. We will focus on issues such as K-means and complex clustering function, Agglomerative (bottom-up), and divisive (top-down) techniques. We will also touch on subjects like principal component analysis (PCA) and how to utilize PCA in the real world.
We will examine various examples of different areas. In the final session, we will walk through several lab sessions on every topic, using various datasets, including all the operations and codes.
Content of the Course:
- Data Science Concept
- R Programming Basics
- Machine Learning Basics
- Predictive Model Building Basics
- Text analytics basics
- Fundamentals of Time Series and Forecasting
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