Unlock the Power of R: Dive into Machine Learning

The realm of Machine Learning has transformed the way we interact with data, and the R programming language remains one of the most powerful and accessible tools for data scientists. This course is crafted to take you step-by-step from the fundamentals to advanced implementation of Machine Learning models in R, with a practical and applicable approach.

You will start with a clear introduction to what Machine Learning is, the various types of learning, and how to set up your working environment. From there, you’ll engage with real datasets and learn how to import, explore, transform, and scale variables to train robust and reliable models. You’ll build and evaluate regression models (Linear, Ridge, Lasso, Trees) and classification models (Logistic Regression, Trees, Random Forest, XGBoost). Additionally, you will master unsupervised clustering techniques like K-Means and apply validation methods such as confusion matrix, cross-validation, and specific metrics to assess your models’ performance.

Moreover, discover how to optimize your models using Grid Search and implement production solutions using plumber to create APIs. As a bonus, learn to integrate ChatGPT for code debugging and function explanation, accelerating your workflow and learning. This course is ideal for students, professionals, and self-learners looking to dive into data science with R, through structured training, real-life cases, and modern tools.

What you will learn:

• Understanding the fundamentals of Machine Learning and its types of learning.
• Implementing regression and classification models using R.
• Applying data preprocessing techniques: cleaning, transformation, and scaling.
• Utilizing algorithms like Decision Trees, Random Forest, XGBoost, and K-Means.
• Evaluating and optimizing models through metrics, cross-validation, and Grid Search.
• Deploying models into production using APIs with plumber and assistance from ChatGPT.

Course Content:

• Sections: 10
• Lectures: 43
• Duration: 4h 44m

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 wanting to apply predictive and classifier models.
• Self-learners seeking a structured and practical 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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