May cohort is now open: How to secure your spot:

Scalable Machine Learning (Part 1)

Scalable Machine Learning (Part 1)

Scalable Machine Learning (Part 1)

This article discusses the practical considerations of scaling machine learning algorithms, such as data storage, feature engineering, and model selection.

Get more great content for data analysis with python.

Data science is the use of advanced methods to extract knowledge from data. This article explains how to use scalable methods to create a data science pipeline. It explains the importance of using the right tools for data collection, pre-processing, modeling, and evaluation. It also provides an overview of the different types of models and how to choose the best one for a particular problem. Finally, it provides an example of how to use the pipeline to build a machine learning model.

Data collection is the first step in the data science pipeline. This involves collecting data from various sources, such as databases, APIs, and web scraping. It is important to use the right tools and techniques to ensure the data is accurate and complete.

The next step is pre-processing. This involves cleaning the data to ensure it is ready to be used in the model. It also involves feature engineering, which is the process of transforming raw data into features that can be used in the model.

The third step is model building. This involves selecting a model type and training it on the data. Different models have different strengths and weaknesses, so it is important to choose the right one for the problem.

Finally, the last step is evaluation. This involves testing the model on unseen data to measure its accuracy and performance. This helps to ensure the model is ready for deployment.

Check out the full post at github.io.