Unlock the Power of Data: Master R Programming for Machine Learning on Udemy

The world of Machine Learning has revolutionized how we interact with data, and R remains one of the most powerful and accessible tools for data scientists. This course is designed to take you step-by-step from the fundamentals to the advanced implementation of Machine Learning models in R, with a practical and applicable focus. You will start with a clear introduction to what Machine Learning is, the types of learning, and how to prepare your working environment. From there, you’ll work with real data, learning how to import, explore, transform, and scale variables to train robust and reliable models.

Throughout the course, you will learn how to build and evaluate regression models (Linear, Ridge, Lasso, Trees) and classification models (Logistic Regression, Trees, Random Forest, XGBoost). Additionally, you’ll master unsupervised clustering techniques like K-Means and apply validation methods such as confusion matrices, cross-validation, and specific metrics to assess the performance of your models. Moreover, you will discover how to optimize your models using Grid Search and implement production solutions using plumber to create APIs. Plus, you’ll learn to integrate ChatGPT for code debugging and function explanations, accelerating your workflow and learning experience.

This course is ideal for students, professionals, and self-taught individuals looking to break into the world of data science with R, through structured training, real-world cases, and modern tools. Whether you are a beginner or looking to enhance your skills, this course provides a comprehensive introduction to Machine Learning and the power of R programming.

What you will learn:

• Understand the fundamentals of Machine Learning and its types of learning.
• Implement regression and classification models using R.
• Apply data preprocessing techniques: cleaning, transformation, and scaling.
• Utilize algorithms such as Decision Trees, Random Forest, XGBoost, and K-Means.
• Evaluate and optimize models through metrics, cross-validation, and Grid Search.
• Deploy models to production through APIs with plumber and ChatGPT assistance.

Course Content:

• Sections: 10
• Lectures: 50
• Duration: 15 hours

Requirements:

• Basic programming knowledge in R.
• Familiarity with basic statistical concepts (regression, variables, distributions).
• R and RStudio should be installed.
• No prior experience in Machine Learning is required.
• Ideal for beginners in machine learning with R.

Who is it for?

• Data analysts and scientists looking to start with Machine Learning using R.
• Students in technical or scientific fields interested in artificial intelligence.
• Professionals working with data who wish to apply predictive and classification models.
• Self-taught individuals seeking a practical and structured introduction to machine learning.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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