Course Overview
Need more information about the Vertex AI Feature Store? Do you want to understand how to transform your ML models to be more accurate? How about identifying the most useful attributes among the data columns? We're glad you're here. In this section, we'll talk about good versus destructive features as well as how to preprocess and convert them for the best use in your models. In this course, you'll learn about feature engineering with BigQuery ML, Keras, and TensorFlow through both content and labs.
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Prerequisites
Familiarity with Python or another programing language
Audience Profile
- Data Analysts
- Data Engineers
- Data Scientists
- ML Engineers
- ML Software Engineers
Learning Objectives
- Utilize the Vertex AI Feature Store
- Explain how to move from raw data to features and perform feature engineering
- Preprocess features using Apache Beam and DataFlow
- Use tf. Transform
Content Outline
An overview of the course's goals is given in this module.
This module introduces the Vertex AI Feature Store.
Feature engineering is frequently the most time-consuming and difficult stage of developing your ML project. In the process of creating features for your machine learning algorithms, you start with your raw data and draw on your domain expertise. This session looks at the qualities of a strong feature and how to express one in your machine learning model.
This session discusses how to execute feature engineering in BigQuery ML and Keras as well as the distinctions between machine learning and statistics. We'll also talk about some cutting-edge feature engineering techniques.
You will discover more about Dataflow, an addition to Apache Beam, in this module. They can both assist you in setting up and running feature engineering and preprocessing.
While feature crosses aren't used much in classical machine learning, they are an essential element of your toolset in newer ML techniques. You will learn how to identify the kind of issues where feature crosses are a potent tool for machine learning in this subject.
TensorFlow Transform (tf. Transform) is a library for preprocessing data with TensorFlow. Tf. Transform is useful for preprocessing that requires a complete pass of the data, such as: - normalizing an input value by mean and stdev - integrating a vocabulary by looking at all input examples for values - bucketing inputs based on the observed data distribution. In this module, we will explore use cases for it.
This module is a summary of the Feature Engineering course.
FAQs
A: Feature engineering is the process of modifying your data set, including addition, deletion, combination, and mutation, in order to enhance the training of your machine learning model and achieve improved accuracy and performance. Practical feature engineering relies on thorough understanding of the business issue and the data sources accessible.
A: Feature Engineering Example: Continuous data
The most common type of data is continuous data. It can take any value from a given range. For example, it can be the price of some product, the temperature in some industrial process, or the coordinates of some object on the map.
A: Feature Engineering encapsulates various data engineering techniques such as selecting relevant features, handling missing data, encoding it, and normalizing it. It is one of the most important tasks and plays a key role in determining the outcome of a model
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are onboarded in the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.
Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
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Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.