Course Machine Learning Toolbox

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

DescriptionChaptersExercisesInstructor

This online course about Machine Learning Toolbox covers a key part of what a future data analyst would require.

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular caret R package, which provides a consistent interface to all of R’s most powerful machine learning facilities, is used throughout the course.

Enroll now in this Machine Learning Toolbox course, and don’t miss the opportunity of learning with the best, as Zachary Deane-Mayer is. With 88 enriching exercises, 24 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Chapter 1: Regression models: fitting them and evaluating their performance
In the first chapter of this course, you’ll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
Chapter 2: Tuning model parameters to improve performance
In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.
Chapter 3: Selecting models: a case study in churn prediction
In the final chapter of this course, you’ll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).
Chapter 4: Classification models: fitting them and evaluating their performance
In this chapter, you’ll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
Chapter 5: Preprocessing your data
In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.
Chapter 6: Regression models: fitting them and evaluating their performance
In the first chapter of this course, you’ll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).
Chapter 7: Classification models: fitting them and evaluating their performance
In this chapter, you’ll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).
Chapter 8: Tuning model parameters to improve performance
In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.
Chapter 9: Preprocessing your data
In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.
Chapter 10: Selecting models: a case study in churn prediction
In the final chapter of this course, you’ll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).
Machine Learning Toolbox. This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

Zachary Deane-Mayer

Automation First Data Scientist at DataRobot

Zach is a Data Scientist at DataRobot and co-author of the caret R package. He’s fascinated by predicting the future and spends his free time competing in predictive modeling competitions. He’s currently one of top 500 data scientists on Kaggle and took 9th place in the Heritage Health Prize as part of the Analytics Inside team.

Collaborators

#R #Python #MachineLearning #BigData #DataAnalysis