Data science

Analyze structured and unstructured data to extract knowledge and insights.

Data science is the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It can be used to make predictions and decisions using analytics and machine learning.

Tutorials

28 November 2024

Tutorial

Updating the knowledge base of a RAG DA chatbot

Learn how to update a RAG chatbot knowledge base with new documents in IBM Cloud to improve real-time accuracy and enhance chatbot performance.

26 November 2024

Tutorial

Deploying RAG Solutions with ElasticSearch Platinum and OpenShift

Learn how to deploy Retrieval-Augmented Generation with ElasticSearch Platinum and Red Hat OpenShift on IBM Cloud for scalable GenAI applications.

07 October 2024

Tutorial

Accelerating Gen AI on IBM Cloud: Deployable Architectures

Discover how RAG Deployable Architecture on IBM Cloud maximizes Gen AI deployment speed, security, and compliance for enterprise solutions.

19 August 2024

Tutorial

Using different LLMs in watsonx.ai flows engine

Learn how to use various models with the watsonx.ai flows engine to create a custom flow for text completion with several different prompting techniques.

05 August 2024

Tutorial

Analyzing and forecasting with time series data using ARIMA models in Python

Learn about how ARIMA models can help you analyze and create forecasts from time series data. Learn how to create and assess ARIMA models using Python in a Jupyter notebook on IBM watsonx.ai platform.

10 July 2024

Tutorial

Implement hierarchical clustering in Python

Implement hierarchical clustering on a real-world data set in Python using Jupyter Notebooks on IBM watsonx.ai. Discover how to identify the optimal number of clusters and visualize the results.

28 May 2024

Tutorial

Creating autoregressive models in Python

Learn how to use autoregressive models to predict time series data. Transform the time series data using differencing so that an autoregressive model could be more easily fit. Develop two different autoregressive models and compared the two models.

20 May 2024

Tutorial

Creating autoregressive models in R

Learn how to use autoregressive models to predict time series data. Develop two different autoregressive models using the AR and ARIMA methods and compare the two of them. After finding the better fit, inverse the Box-Cox transform to get untransformed values of the prediction.

15 May 2024

Tutorial

Implement XGBoost in R

Learn all about the XGBoost algorithm and how it uses gradient boosting to combine the strengths of multiple decision trees for strong predictive performance and interpretability.

15 May 2024

Tutorial

Implement XGBoost in Python

Learn how to use the XGBoost Python package to train an XGBoost model on a data set to make predictions. Then, learn how to do hyperparameter tuning to find the optimal hyperparameters for our model.

14 May 2024

Tutorial

Analyzing and forecasting with time series data using ARIMA models in R

Learn about how ARIMA models can help you analyze and create forecasts from time series data. Learn how to create and assess ARIMA models using R in a Jupyter notebook on IBM watsonx.ai platform with a data set imported from the FPP library, which is one of the canonical time series libraries in R.

26 March 2024

Tutorial

Linear regression in R

In this tutorial, learn how to use the Cars data set included with R and create a linear regression model that can provide estimates of the effect that independent variables have on a dependent variable. Also, learn strategies to validate models and deal with interactions between variables and outliers in your data.

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Data science

Analyze structured and unstructured data to extract knowledge and insights.

Tutorials

Updating the knowledge base of a RAG DA chatbot

Deploying RAG Solutions with ElasticSearch Platinum and OpenShift

Accelerating Gen AI on IBM Cloud: Deployable Architectures

Using different LLMs in watsonx.ai flows engine

Analyzing and forecasting with time series data using ARIMA models in Python

Implement hierarchical clustering in Python

Creating autoregressive models in Python

Creating autoregressive models in R

Implement XGBoost in R

Implement XGBoost in Python

Analyzing and forecasting with time series data using ARIMA models in R

Linear regression in R