Feature Engineering for Machine Learning in Python
Create new features to improve the performance of your Machine Learning models.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
Create new features to improve the performance of your Machine Learning models.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
You will investigate a dataset from a fictitious company called Databel in Tableau, and need to figure out why customers are churning.
Learn the fundamentals of AI security to protect systems from threats, align security with business goals, and mitigate key risks.
Improve data literacy skills by analyzing remote working policies.
Learn to perform linear and logistic regression with multiple explanatory variables.
Boost your Excel skills with advanced referencing, lookup, and database functions using practical exercises.
In this course, you will be introduced to unsupervised learning through techniques such as hierarchical and k-means clustering using the SciPy library.
Learn how to create pivot tables and quickly organize thousands of data points with just a few clicks.
Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.
Learn how to use MLflow to simplify the complexities of building machine learning applications. Explore MLflow tracking, projects, models, and model registry.
Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.
Take your R skills up a notch by learning to write efficient, reusable functions.
Master multi-stage builds, Docker networking tools, and Docker Compose for optimal containerized applications!
This course focuses on feature engineering and machine learning for time series data.
In this course, youll learn about the concepts of random variables, distributions, and conditioning.
Learn how to perform financial analysis in Power BI or apply any existing financial skills using Power BI data visualizations.
Learn about Large Language Models (LLMs) and how they are reshaping the business world.
Learn to create your own Python packages to make your code easier to use and share with others.
Learn about string manipulation and become a master at using regular expressions.
Get your AI Act together! Understand the obligations, risks, and requirements of the EU AI Act.
Bash scripting allows you to build analytics pipelines in the cloud and work with data stored across multiple files.
Learn how to clean data with Apache Spark in Python.
Explore AI and data monetization strategies, build ethical infrastructures, and align products with business goals.
Build up your pandas skills and answer marketing questions by merging, slicing, visualizing, and more!
Learn how to build your own SQL reports and dashboards, plus hone your data exploration, cleaning, and validation skills.
In this conceptual course (no coding required), you will learn about the four major NoSQL databases and popular engines.
Master sampling to get more accurate statistics with less data.
In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.