Classification models are used in the real world to predict whether to buy, sell, or hold a particular stock or to identify objects in images. RapidMiner studio supports features such as Turbo Prep and Auto Model that completely automate data processing and model building. In this course, discover how classification models can be used to categorize input records and how metrics such as accuracy, precision, and recall can be used to evaluate those classification models. Next, create a process to retrieve, summarize, and visualize data using operators. Finally, configure your own workflow for classification, and train and compare a logistic regression model and a random forest model. You will choose the best-performing model for local deployment on your machine and see how you can use deployed models for predictions. Once you have completed this course you will have the skills to train, clean, and process data in order to train classification models and deploy your model locally.

The more organizations depend on data for decision making, the more important machine learning becomes in every business process. The RapidMiner data science platform allows users to build complex analytics workflows with little to no code. Through this course, learn how to get started with RapidMiner. Discover what support RapidMiner offers for the analytics and artificial intelligence workflow, as well as the various tools included with RapidMiner. Next, explore the basics of machine learning and compare supervised and unsupervised learning models. Finally, work with RapidMiner Studio, and learn about the tool's different panels. Upon completion, you'll be able to set up to build predictive models in RapidMiner.

Clustering models work with unlabeled data, finding logical groupings in data, and are often used for social media ad targeting and document discovery. In this course, explore the clustering unsupervised learning technique. Next, retrieve data from the repository into your process and use Turbo Prep to clean and preprocess the data for clustering analysis. Then use Auto Model to train k-means and x-means clustering models on your data and evaluate and visualize the models created. Finally, create your own analytics process for k-means clustering, evaluate your model using the Davies-Bouldin score, use principal component analysis (PCA) to better visualize the clusters found in your data, and determine the ideal number of clusters by using hyperparameter tuning. When you are finished with this course, you will be able to fit and evaluate clustering models on your data and visualize clusters with data points plotted using principal components.

Regression is used in the real world to predict things like stock prices, car mileage, or insurance premiums. RapidMiner studio offers an easy-to-use visual designer that allows you to construct a regression workflow with little to no code. In this course, explore regression models and the R-squared metric used to evaluate regression models. Next, use RapidMiner to retrieve data and use it for modeling. Then, automate data preparation with Turbo Prep, automate the training of multiple regression models using Auto Model, and compare these models using RapidMiner. Build a workflow to train regression models by using operators for data cleaning, imputing missing values, one-hot encoding, and partitioning your data. Finally, train multiple models for regression analysis and compare their performance and perform hyperparameter tuning to get the best model design for your use case. When you are finished with this course, you will be able to build a complete workflow in RapidMiner for regression analysis and improve your model using hyperparameter tuning.

Time series forecasting uses data collected over periodic intervals to analyze how the variable changes over time. Time series analysis is often used for forecasting problems such as demand forecasting and revenue forecasting. In this course, discover how time series analysis works and how time series models such as the autoregressive integrated moving average (ARIMA) model can help forecast future values of time-varying data using historical values. Next, visualize and explore time series data using windowing, differencing, moving averages, and time series decomposition. Then fit a function, a seasonal component model, and an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a grocery store and perform association rule learning on this data to figure out what items are frequently bought together. When you are finished with this course you will have the skills to use RapidMiner for time-series forecasting and market basket analysis.