This course covers the use of PyTorch to build various predictive models, using Recurrent Neural Networks, long-memory neurons in text prediction, and evaluating them using a metric known as the Mean Average Precision @ K.

Dive into predictive analytics with SQL Server. This course will teach you to construct models, apply regression, and utilize smoothing techniques for insightful time series analysis.

Learn predictive data analysis techniques using Python, focusing on forecasting, modeling, and data-driven decision-making strategies.

Dive into predictive data analysis techniques using Python, focusing on building models and making data-driven forecasts.

Learn predictive modeling techniques for real-world analytics. Master regression, classification, and forecasting methods to make accurate predictions based on historical data in fields like business, healthcare, and finance.

You'll learn how to use Power Query to extract data from different data sources, choose a storage mode, and connectivity type. You'll also learn to profile, clean, and load data into Power BI before you model your data.

Designing reports for enterprise scale requires more than just connecting to data. Understanding Power BI model frameworks and strategies for scalability and optimization are key to a successful enterprise implementation. This learning path helps you prepare for the Azure Enterprise Data Analyst Certification.

Explore the role of a database administrator on Azure. Describe SQL Server-based offerings on Azure.

data analysis

This course covers categories of feature engineering techniques used to get the best results from a machine learning model, including feature selection, and several feature extraction techniques to re-express features in the most appropriate form.

This course covers important techniques in data preparation, data cleaning and feature selection that are needed to set your machine learning model up for success. You will also learn how to use imputation to deal with missing data and strategies for identifying and coping with outliers.

This course covers important steps in the pre-processing of data, including standardization, normalization, novelty and outlier detection, pre-processing image and text data, as well as explicit kernel approximations such as the RBF and Nystroem methods.