Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

By using anonymized data from real and animated basketball players in the upcoming film Space Jam: A New Legacy, create a machine learning model to cleanse data to be used during games. Also explore bimodal data distributions by using Python in Visual Studio Code.

Learn how to predict Bitcoin’s closing prices using foundational machine learning models. Understand the basics of time series forecasting and apply regression models to forecast cryptocurrency trends and investment opportunities.

Learn how to use concepts from machine learning to predict the occurrence of meteor showers (or Moon Goddess tears).

Learn how to predict sea-rise risks using geospatial APIs. Understand how to work with geospatial data to model sea level rise, assess flood risks, and make informed decisions about climate change impact and disaster preparedness.

Explore how weather data can be used to predict stock market trends. Learn to combine financial data with meteorological insights to identify patterns and forecast stock movements, offering a unique approach to market analysis and trading strategies.

Predict taxi tips using Scikit-Learn and Snap ML. Learn to preprocess data, apply machine learning algorithms, and evaluate model performance to build an accurate predictive model for taxi service data.

Learn regression techniques for predicting car mileage and diamond prices. Master statistical modeling, feature engineering, and evaluation metrics to create accurate predictions based on real-world data in the automotive and luxury sectors.

Learn to predict future outcomes and trends through statistical modeling techniques.

This course will teach you to understand and implement important techniques for predictive analytics such as regression and classification using Apache Spark MLlib APIs on Databricks.

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network