No/Low Code Machine Learning

Organizations have been collecting data for analytics and predictive modeling for decades, however, in the past, this analysis has been restricted to engineers and analysts who can write code. The KNIME Analytics Platform makes machine learning and data analytics more accessible by allowing you to build complex workflows with little to no code. Through this course, learn how the KNIME platform works. Examine the role of the KNIME Analytics Platform and the KNIME Community Hub. Next, explore machine learning basics and how supervised and unsupervised learning techniques work. Finally, discover how to set up the KNIME Analytics Platform and get familiar with the KNIME user interface. Upon completion, you'll be able to handle building machine learning workflows using KNIME.

Regression analysis is used to predict continuous data values. The KNIME Analytics Platform allows you to load, explore, pre-process, and use data to train regression models with little to no code. Through this course, learn how to train and evaluate regression models in KNIME. Explore how regression models work and use KNIME nodes to build a workflow to load and comprehend data. Next, discover how to compute correlations and use bar charts, box plots, scatter plots, and pivot tables. Finally, learn how to pre-process flight prediction data using one-hot and label encoding, partition data, and train regression models. After course completion, you'll be able to build a complete workflow in KNIME for regression analysis.

Classification models are used to categorize data into a fixed number of discrete classes or categories. The KNIME Analytics Platform allows you to load, explore, pre-process, and use your data to train classification models with little to no code. In this course, explore classification models and the metrics used to evaluate their performance. Next, construct a KNIME workflow to load and view the data for a classification model. You will clean data, impute missing values, and cap and floor outlier values in a range. Then you will identify and filter correlated variables and you will convert categorical data to numeric values and express numeric variables. Finally, train several different classification models on the training data, evaluate them using the test data, and select the best model using hyperparameter tuning. Upon completing this course, you will have the skills and knowledge to train, clean, and process your data and to use that data to train classification models and perform hyperparameter tuning.

Clustering is an unsupervised learning technique that finds logical groupings or clusters in your data, for example, identifying what social network users have the same interests and background. In this course, explore how clustering models seek to find logical groupings in your data. Next, construct a KNIME workflow to load and explore data for a clustering model. Then, fill in missing values using different imputation techniques, identify highly correlated variables, and deal with outliers. Fit a k-means clustering model on your data, identify clusters, and use scatter plots to visualize the clusters in your data. Finally, perform dimensionality reduction using principal component analysis (PCA) and use the silhouette score to evaluate the number of clusters that gives you the best clustering for your data. Upon course completion, you will be able to fit and evaluate clustering models on your data and visualize clusters using 2-D and 3-D visualizations.

Organizations use time series analysis and market basket analysis to understand patterns over time. Time series analysis uses data collected over regular intervals to analyze how the variable changes over time, while market basket analysis is an application of association rule learning that tries to learn what items occur together frequently in the same transaction. In this course, discover how time series analysis works and how time series models like the autoregressive integrated moving average (ARIMA) model can help you forecast future values of time-varying data using historical values. Next, visualize time series data using moving averages and time series decomposition and fit 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 bakery and perform association rule learning on this data to figure out what items are frequently bought together. Upon course completion, you will be able to confidently use KNIME for time series analysis and market basket analysis.

Final Exam: Low-code Machine Learning with KNIME will test your knowledge and application of the topics presented throughout the Low-code Machine Learning with KNIME track.

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.

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.

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.

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.

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.

Final Exam: No-code Machine Learning with RapidMiner will test your knowledge and application of the topics presented throughout the No-code Machine Learning with RapidMiner track.

BigQuery is a flagship product on the Google Cloud Platform which allows you to build and train machine learning (ML) models using simple SQL queries. BigQuery has support for a range of supervised and unsupervised machine learning models that can be trained on data stored in BigQuery. In this course, you will be introduced to BigQuery on the Google Cloud Platform and set up a GCP trial account that allows you to work with BigQuery to train ML models. You will then review some machine learning basics and dig a little deeper into regression models. Next, you will create datasets and tables in BigQuery and upload your data to the cloud. You will visualize and explore your data using Looker Studio and prepare and clean your data using DataPrep. Finally, you will train regression models using linear regression, gradient-boosted trees, and the random forest model and evaluate and compare the performance of these models on your test data.

Predictive models that output discrete classes or categories are classification models. Classification is widely used in the real world for use cases such as sentiment analysis of text and identifying objects in images. In this course, you will review how classification models can be used to categorize or classify input records. You will learn how metrics such as accuracy, precision, and recall can be used to evaluate classification models and the conditions under which you would choose to use precision and recall over accuracy for model evaluation. Next, you will use the BigQuery command-line tool bq to create a BigQuery dataset and table and load data into that table. You will see how you can run queries and explore your data, all using the command line. You will use Looker Studio for data visualization and DataPrep to clean and prepare your classification data. Finally, you will train a binary classification model and a multi-class classification model. You will improve the model's performance by balancing the records in the different categories and by using hyperparameter tuning to find the best model for your data.

Unsupervised techniques such as clustering and recommendation systems can discover patterns in unlabeled data. These models extract structure in the x-variables or features present in the data. In this course, you will work with two unsupervised learning methods, clustering and recommendation systems. You will explore how clustering algorithms use only the x-variables or features in your data to group data into logical clusters. Then you will discover the basic concepts behind recommendation systems, which take in past user interactions with products and use that to recommend new products to users. Next, you will train a clustering model using k-means clustering on your data and evaluate how the clusters differ. You will use hyperparameter tuning to find the best number of clusters on your dataset. Finally, you will train a recommendations engine using collaborative filtering and use that to make movie recommendations to users based on their past preferences and the preferences of other users.

Time series forecasting uses data collected over periodic intervals to understand and analyze how the variable changes over time. Time series analysis is used for forecasting problems, such as demand forecasting and revenue forecasting. The auto-regressive integrated moving average (ARIMA) model is widely used for time series forecasting. In this course, you will see how time series analysis works and how models such as the ARIMA model can help you forecast future values of time-varying data using historical values. You will also learn the differences between stationary and non-stationary time series data. Next, you will load and explore your time series data for store revenue prediction into BigQuery and visualize and explore this data using Looker Studio. Finally, you will use an ARIMA model to make revenue forecasts. You will see how BigQuery ML trains multiple ARIMA models to find the best auto-regressive, differencing, and moving average parameters for your data. You will also perform multiple time-series analysis by forecasting store revenue by region.

Final Exam: Machine Learning Using SQL with BigQuery ML will test your knowledge and application of the topics presented throughout the Machine Learning Using SQL with BigQuery ML track.