Course Overview
This course provides a definition of machine learning and explains how it can help your company. You'll watch a few demonstrations of machine learning in action and understand key concepts like instances, features, and labels. In the interactive labs, you will get practise utilising the pre-trained ML APIs that are accessible and create your own Machine Learning models with BigQuery ML using only SQL.
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Prerequisites
- We recommend participants have some knowledge of ANSI SQL.
- To get the most advantage out of this course, participants must complete the introductory courses in this specialization:
- Exploring and Preparing your Data
- Storing and Visualizing your Data
- Architecture and Performance
Audience Profile
Data Analysts, Business Analysts, and Business Intelligence professionals Cloud Data Engineers will partner with Data Analysts to build scalable data solutions on the Google Cloud Platform.
Learning Objectives
- Describe the concept of machine learning and how it can help your business.
- Identify the differences between pre-trained and unique ML models.
- In BigQuery, create machine learning datasets.
- In BigQuery, create machine learning models.
Content Outline
Introduction of what you will learn in this course
This module defines machine learning and how it can help your business. You'll see a view demos of ML in action and learn critical terms like instances, features, and labels.
This module will discuss pre-built and pre-trained ML models that can be accessced (like image recognition and sentiment analysis) utilizing Cloud Data lab.
Understand how tobuild ML datasets utilizing BigQuery.
You will discover how to build machine learning models right inside BigQuery in this subject. You will become familiar with the new syntax as you build, assess, and test an ML model.
You've reached the final stage Let's go through the course's lessons learnt and the resources accessible for further study.
FAQs
A: Businesses can use machine learning to allow the data to teach the system how to use machine learning algorithms to solve the current problem—and how to get better over time andinstances of machine learning
Here are a few applications for machine learning:
- Robotic process automation (RPA)
RPA and machine learning work together to develop intelligent automation that can handle difficult jobs like processing mortgage applications.
- Sales optimization
Machine learning algorithms for customer sentiment analysis, sales forecasting analysis, and customer churn predictions can be trained using customer data.
- Customer service
Chatbots and automated virtual assistants are examples of machine learning technologies that can automate repetitive customer care duties and hasten problem solving.
- Security
Enterprises may strengthen their threat analysis skills and their ability to respond to malware, hackers, and cyberattacks by using security machine learning.
- Digital Marketing
Digital marketing machine learning enables advertisers to find new clients and provide the correct prospects with marketing collateral at the right moment.
- Fraud mitigation
Banks and credit card companies utilize machine learning to analyse enormous volumes of transactional data and spot questionable behaviour in real time.
A: Our services are fast, scalable, and easy to use. Major Google applications use Cloud machine learning, including Photos (image search), the Google app (voice search), Translate, and Inbox (Smart Reply).
A: Businesses can use machine learning to enable the data to teach the system how to use machine learning algorithms to solve the current problem—and how to get better over time.
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 would always 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.
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.