new machine learning courses!

Whether you are working in R or Python, we have you covered! In Unsupervised Learning, you will build algorithms to find patterns in data. In Supervised Learning with scikit-learn, you will design algorithms to make predictions based on data. Start now! [DataCamp]( New Courses! [Unsupervised Learning]( Unsupervised Learning in R Taught by Hank Roark, Senior Data Scientist at Boeing The goal of unsupervised learning is to find patterns in data without trying to make predictions. This course provides a basic introduction to important concepts in unsupervised learning such as clustering and dimensionality reduction in R. [Start Learning ›]( Supervised Learning with scikit-learn Taught by Andreas Müller, Core Developer of scikit-learn In this course, you'll use Python to perform supervised learning, an essential component of Machine Learning. You'll learn how to build predictive models, how to tune their parameters, and how to tell how well they will perform on unseen data, all while using real world datasets. [Start Learning ›]( Unsupervised Learning in R: What You'll Learn Chapter One: Unsupervised Learning in R Learn about the k-means algorithm and implement your own k-means clustering on real world data. Chapter Two: Hierarchical Clustering Hierarchical clustering is another popular method for clustering. Learn how it works, how to use it, and compare it to k-means clustering. Chapter Three: Dimensionality Reduction with PCA Principal component analysis, or PCA, is a common approach to dimensionality reduction. Chapter Four: Putting it all Together (case study) In this chapter you will perform a complete analysis using unsupervised learning techniques. [Play now ›]( Supervised Learning with scikit-learn: What You'll Learn Chapter One: Classification Learn how to solve classification problems using supervised learning techniques. Chapter Two: Regression Learn about fundamental concepts in regression and apply them to predict the life expectancy in a given country. Chapter Three: Fine-tuning Your Model Learn to optimize both your classification and regression models using hyperparameter tuning. Chapter Four: Preprocessing & Pipelines Use pre-processing techniques as a way to enhance model performance. [Play now ›]( DataCamp Inc. 2067 Massachusetts avenue Cambridge MA 02141 [Unsubscribe](