Course Overview
The first topic covered in the course is data, specifically how to conduct exploratory data analysis and enhance data quality. In this article, we'll go through Vertex AI AutoML and show you how to create, train, and use ML models without writing a single line of code. You will comprehend Big Query ML's advantages. We next go over machine learning (ML) model optimization and how generalization and sampling can be used to evaluate the quality of ML models for specific training.
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Prerequisites
• Familiarity with Python or another programing language
Audience Profile
- Aspiring machine learning data scientists and engineers
- Machine learning scientists, data scientists, and data analysts
- Data Engineer
Learning Objectives
- Describe ways to enhance the quality of your data and conduct deep data analysis.
- Utilizing Vertex AI and BigQuery ML, create and train AutoML models.
- Utilize loss functions and performance indicators to improve and assess your models.
- Make training, evaluation, and test datasets that are scalable and reproducible.
Content Outline
An overview of the course's goals is provided in this module.
In this session, we examine how to enhance the quality of our data as well as how to conduct exploratory data analysis on it. We examine tidy data's significance in machine learning and demonstrate how it affects data quality. For instance, missing values can influence our findings. Additionally, you will discover how crucial data exploration is. You'll run an exploratory data analysis on the dataset once we've cleaned up the data.
To help you advance as a practitioner of machine learning, we will introduce some of the main categories of ML in this session.
This session introduces utilizing Vertex AI to train AutoML models.
We will introduce BigQuery ML and its features in this module.
This module will walk you through how to optimize your ML models.
It's time to respond to a strange query: When should you use a different machine learning model despite it being the most accurate? Just because a model has a loss measure of 0 for your training dataset does not guarantee that it will perform well on new data in the real world, as we hinted at in the previous session on optimization. You will discover how to develop performance benchmarks and make reproducible training, assessment, and test datasets.
The Launching into Machine Learning course is summarised in this lesson.
FAQs
A: The need for better data is the main issue facing machine learning. Although improving algorithms frequently takes up the majority of developers' effort in AI, data quality is crucial for the algorithms to work as intended.
A: The number one problem facing Machine Learning is the need for better data. While enhancing algorithms often consumes most of the developers' time in AI, data quality is essential for the algorithms to function as intended.
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 a 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 after the training program.
A: To attend the training session, you should have active 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, due to some contingency, if 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.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participants' hand-on-practice. 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 industry Oriented 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.