Machine Learning In Power Bi
Machine Learning in Power BI empowers users to harness the power of artificial intelligence within t
Machine Learning In Power Bi
Machine learning in Power BI enables organizations to leverage data insights, automate tasks, and enhance decision-making by harnessing the power of predictive analytics. It empowers users to create and deploy machine learning models within Power BI, allowing them to forecast future trends, identify patterns, and make informed predictions based on historical data.
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Machine learning in Power BI enables organizations to leverage data insights, automate tasks, and enhance decision making by harnessing the power of predictive analytics. It empowers users to create and deploy machine learning models within Power BI, allowing them to forecast future trends, identify patterns, and make informed predictions based on historical data.
Course Overview
“Machine Learning in Power BI” empowers data analysts with the ability to enhance data analysis and decision-making through the integration of machine learning techniques into Power BI. This course provides hands-on experience with using ML algorithms like regression, classification, and clustering, enabling users to build predictive models, classify data, and identify patterns and outliers. By the end of the course, participants will gain proficiency in leveraging Power BI's ML capabilities to uncover valuable insights, automate tasks, and improve the accuracy of their data analysis and reporting.
Course Description
This course introduces the fundamentals of machine learning and its applications in Power BI. It covers concepts like supervised and unsupervised learning, regression, classification, clustering, and anomaly detection. Students will learn to use Power BI's built-in machine learning capabilities to enhance data analysis and decision-making, including creating predictive models and using machine learning algorithms to automate insights.
Key Features
1 - Comprehensive Tool Coverage: Provides hands-on training with a range of industry-standard testing tools, including Selenium, JIRA, LoadRunner, and TestRail.
2) Practical Exercises: Features real-world exercises and case studies to apply tools in various testing scenarios.
3) Interactive Learning: Includes interactive sessions with industry experts for personalized feedback and guidance.
4) Detailed Tutorials: Offers extensive tutorials and documentation on tool functionalities and best practices.
5) Advanced Techniques: Covers both fundamental and advanced techniques for using testing tools effectively.
6) Data Visualization: Integrates tools for visualizing test metrics and results, enhancing data interpretation and decision-making.
7) Tool Integration: Teaches how to integrate testing tools into the software development lifecycle for streamlined workflows.
8) Project-Based Learning: Focuses on project-based learning to build practical skills and create a portfolio of completed tasks.
9) Career Support: Provides resources and support for applying learned skills to real-world job scenarios, including resume building and interview preparation.
10) Up-to-Date Content: Ensures that course materials reflect the latest industry standards and tool updates.
Benefits of taking our course
Functional Tools
Tools Used in ‘Machine Learning in Power BI’ Course:
1 - Power BI Desktop: A comprehensive data analytics and visualization platform that supports machine learning capabilities. It allows users to access, transform, and analyze data, as well as create interactive dashboards and reports that can leverage machine learning models.
2) Azure Machine Learning: A cloud based platform that offers a range of machine learning services, including model training, deployment, and management. It provides a user friendly interface and powerful tools for building and operationalizing machine learning solutions within the Power BI platform.
3) Python: A widely used programming language known for its data science and machine learning capabilities. Power BI supports Python scripting, which allows users to extend the functionality of the platform by writing custom code for data manipulation, model creation, and more.
4) R: Another popular programming language specifically designed for statistical computing and data analysis. Power BI integrates with R, enabling users to leverage R scripts and packages for advanced data analysis, model building, and visualization tasks.
5) SQL: A standard language used for managing and querying relational databases. Power BI natively supports SQL, allowing users to connect to and access data from various relational data sources, such as SQL Server, Oracle, and MySQL.
6) Data Science Connector: A custom connector for Power BI that provides a bridge between the platform and external data science tools and services. It enables users to import models and results from tools like Azure Machine Learning, Python, and R into Power BI reports for further analysis and visualization.
Training Program for Students:
- Introduction to machine learning concepts and applications in Power BI
- * Hands on experience with the Power BI platform and Azure Machine Learning services
- * Guidance on data preparation, model building, and interpretation
- * Practical exercises involving real world business scenarios
- * Best practices for operationalizing machine learning models within Power BI
- * Support from experienced trainers and access to online resources
- Answer questions thoroughly and accurately.* This is the most important factor in getting points. Make sure your answers are clear, concise, and provide all the necessary information.
- Be the first to answer questions.* You get more points for answering questions early, so try to be one of the first people to respond.
- Up vote good answers.* If you see a good answer, up vote it. This will help it get more exposure and earn the answerer more points.
- Down vote bad answers.* If you see a bad answer, down vote it. This will help it get less exposure and earn the answerer fewer points.
- Report abusive users.* If you see someone who is being abusive or spamming, report them. This will help keep the community clean and safe.
- Be active in the community.* The more you participate in the community, the more points you will earn. Answer questions, ask questions, and comment on other people's answers.
- Join a team.* Joining a team can help you earn more points by working together with other members.
- Attend live events.* Live events are a great way to earn points and connect with other members of the community.
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Contact Info:
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