Data, AI, and Cloud Courses

Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.

Learn to analyze financial statements using Python. Compute ratios, assess financial health, handle missing values, and present your analysis.

Enhance your KNIME skills with our course on data transformation, column operations, and workflow optimization.

Learn how computers work, design efficient algorithms, and explore computational theory to solve real-world problems.

Master Databricks with Python: learn to authenticate, manage clusters, automate jobs, and query AI models programmatically.

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Learn how to write effective tests in Java using JUnit and Mockito to build robust, reliable applications with confidence.

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

Leverage tidyr and purrr packages in the tidyverse to generate, explore, and evaluate machine learning models.

Learn tools and techniques to leverage your own big data to facilitate positive experiences for your users.

Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.

Learn how bonds work and how to price them and assess some of their risks using the numpy and numpy-financial packages.

Learn how to reduce training times for large language models with Accelerator and Trainer for distributed training

Create a healthcare AI agent using Haystack, an open-source framework for orchestrating LLMs and external components.

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

Learn how to use Python parallel programming with Dask to upscale your workflows and efficiently handle big data.

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Explore a range of programming paradigms, including imperative and declarative, procedural, functional, and object-oriented programming.

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

From customer lifetime value, predicting churn to segmentation - learn and implement Machine Learning use cases for Marketing in Python.

Learn how to pull character strings apart, put them back together and use the stringr package.

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

Learn about MLOps, including the tools and practices needed for automating and scaling machine learning applications.

In this course, youll learn how to collect Twitter data and analyze Twitter text, networks, and geographical origin.

Automate data manipulation with KNIME, mastering merging, aggregation, database workflows, and advanced file handling.

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Learn how to import, clean and manipulate IoT data in Python to make it ready for machine learning.