Engineering Features for Machine Learning
In this course we will introduce a number of techniques used to engineer your features. Accompanying each enrichment method description is a brief outline of their relevance for behavioural predictions.
Example run-through of concatenating features
There are limitless feature engineering option possibilities to choose from. With ecosystem.Ai, there are a handful of default options that come with the workbench. However, any number of custom feature engineering options can be included in your personalised version of the product. This course will provide you with information about many of the options, as well as how best to use them to improve your data for machine learning.
Through this course and experience, your skills will naturally improve.
What is Feature Engineering
Feature Engineering is the art of data science. It is the process of creating new features from existing ones in your data.
Most raw data is messy, cleaning allows you to organise and subtract redundant documents, while feature engineering is the addition of new documents. This process helps to highlight important data for your algorithms.
In light of improving model performance, the isolation and addition of polished data ensures the algorithm will generate more accurate predictions.
Feature Engineering options
Useful engineering options include being able to merge two or more features into one, adding additional date information to an existing transaction, amongst others. In lesson two, we will outline and describe the default list provided in the ecosystem workbench.
The reason behind engineering data
The need for high quality model creation informs the need for well defined data inputs. Initial data preparation is vital for success when attempting to achieve the best machine learning results.
Learn more about the Fundamentals of Data Science and Machine Learning:
Data Science and Feature Engineering
2 lessons in course
Frequently Asked Questions
Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
What is a feature store used for?
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
The Feature Store for machine learning is a feature computation and storage service that enables features to be registered, discovered, and used both as part of ML pipelines as well as by online applications for model inferencing.
Why do I need to data engineering?
As a broad field of data, engineering requires you to master a long spectrum of the skills which is not possible for an individual data engineer. We have some specific roles and duties shortlisted that are mainly required by data engineers in different companies. Data engineers mainly fall into three roles in any firm. These roles are:
1. Database-Centric: Typically in bigger corporations, management of data flow is a full-time job and data engineers on this job focus entirely on analytics database. As database-centric data engineers, you will be required to work across multiple databases and develop table schemas in the data warehouse.
2. Pipeline-Centric: In medium-sized companies, data engineers usually work side by side with data scientists to utilize the collected data usefully. As a pipeline-centric data engineer, you will be responsible for working with computer science and distributed system thus in-depth knowledge for both these will be required.
3. Generalist: Smaller Teams and Companies often hire generalist data engineers to work on multiple tasks such as entire data process, from management to analysis. A general data engineer has to wear many hats which are data-focused but not very in-depth. This role is ideal for individuals transitioning from the field of data science to data engineering.