Welcome to your gateway of knowledge in Applied Machine Learning! Whether you’re an advanced data scientist or just a curious learner, this blog serves as your compass to navigate the exciting world of machine learning, especially in the R programming environment.
Overview of the Course
The RStudio conference on January 27 and 28, 2020, unfolded an amazing two-day course on machine learning. This event showcased how to use R for supervised learning—focusing on building, visualizing, and comparing models that are targeted for prediction.
Machine learning is akin to teaching a child how to recognize a fruit. At first, the child might look at a range of different objects—some are apples, some are oranges, and some could even be tomatoes. Through guidance, the child learns to identify patterns. Similarly, machine learning algorithms sift through data, learning from patterns to make predictions.
Learning Objectives
- Utilize the tidymodels packages for creating, tuning, fitting, and assessing models designed for predictions.
- Understand both high-level and low-level modeling approaches using the tidyverse.
- Explore various predictive models through real-world case studies.
Is This Course for You?
This workshop welcomes attendees who have a basic understanding of R and the tidyverse. If you are comfortable with basic R, you will find this course very beneficial.
Prework Requirements
To prepare for the workshop, it is advisable to review some predictive modeling fundamentals. Check out:
We will be utilizing RStudio server pro with pre-installed packages. However, if you wish to run R locally, follow these installation instructions:
install.packages(c(
"AmesHousing",
"C50",
"devtools",
"discrim",
"earth",
"ggthemes",
"glmnet", # See important note below
"klaR",
"lubridate",
"modeldata",
"party",
"pROC",
"rpart",
"stringr",
"textfeatures",
"tidymodels"
), repos = "http://cran.rstudio.com")
devtools::install_github(c(
"tidymodels/tidymodels",
"tidymodels/tune",
"tidymodels/textrecipes",
"koalaverse/vip",
"gadenbuie/countdown"))Important note! A new version of glmnet was released on November 9, 2019. Although it states it requires R (≥ 3.5.0), it may not install on R versions 3.6.0. Make sure to check your R version to avoid issues.
Workshop Schedule
| Time | Activity |
|---|---|
| 09:00 – 10:30 | Session 1 |
| 10:30 – 11:00 | Coffee Break |
| 11:00 – 12:30 | Session 2 |
| 12:30 – 13:30 | Lunch Break |
| 13:30 – 15:00 | Session 3 |
| 15:00 – 15:30 | Coffee Break |
| 15:30 – 17:00 | Session 4 |
Troubleshooting Instructions
If you run into issues during installation or the workshop, we will be available on-site 30 minutes before the sessions to assist you. For prior inquiries, feel free to reach out to max@rstudio.com.
For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.
Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
Join us in this journey of discovery and innovation in the thrilling realm of Applied Machine Learning!