Power Bi Automated Machine Learning
Power BI Automated Machine Learning (AutoML) is a feature that empowers data analysts and business u
Power Bi Automated Machine Learning
Power BI Automated Machine Learning (AutoML) empowers users to create sophisticated machine learning models without extensive coding experience. Its intuitive interface enables businesses to leverage the power of AI and gain insights from their data by automating the model creation process, allowing even non-technical users to derive predictions and solve complex problems with ease.
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Power BI Automated Machine Learning (AutoML) empowers users to create sophisticated machine learning models without extensive coding experience. Its intuitive interface enables businesses to leverage the power of AI and gain insights from their data by automating the model creation process, allowing even non technical users to derive predictions and solve complex problems with ease.
Course Overview
Power BI Automated Machine Learning empowers you to build and deploy machine learning models without the need for extensive technical expertise. This course will guide you through the process of preparing data, selecting algorithms, training models, and evaluating results using Power BI's user-friendly interface. Discover the power of automated feature engineering, algorithm selection, and model optimization to enhance your data analysis and decision-making capabilities.
Course Description
Power BI Automated Machine Learning empowers citizen data scientists with the ability to build and deploy accurate machine learning models. This course explores the principles, techniques, and best practices for leveraging Power BI's automated machine learning capabilities. Through hands-on exercises and real-world examples, learners will discover how to prepare data, select appropriate algorithms, interpret model results, and operationalize models to automate decision-making and improve business outcomes.
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 Power BI Automated Machine Learning
1 - Power BI Desktop: A desktop application that enables users to create and analyze data models, reports, and dashboards. It provides a drag and drop interface for data manipulation and visualization.
2) Power BI Service: A cloud based service that allows users to share and collaborate on Power BI content. It offers features for data storage, security, and governance.
3) Machine Learning Algorithms: A collection of statistical and mathematical models used to identify patterns and make predictions. Power BI Automated Machine Learning uses algorithms such as decision trees, random forests, and gradient boosting machines to identify the most relevant factors and generate accurate predictions.
4) Automated Feature Engineering: A process that automatically transforms and prepares data for machine learning models. Power BI Automated Machine Learning includes a range of feature engineering techniques that improve model performance and reduce the need for manual data preprocessing.
5) Model Evaluation: A set of metrics used to assess the performance of machine learning models. Power BI Automated Machine Learning provides metrics such as accuracy, precision, recall, and F1 score to help users evaluate the quality of their models.
Training Program for Students
1 - Course Overview
Provide an introduction to the concepts of data science and machine learning.
Explain the capabilities of Power BI Automated Machine Learning.
2) Hands on Exercises
Guide students through creating and analyzing data models in Power BI Desktop.
Demonstrate the process of training and evaluating machine learning models using Power BI Automated Machine Learning.
Focus on practical applications and real world scenarios.
3) Project Based Learning
Assign group projects that require students to apply the concepts learned in the course.
Provide guidance and support as students develop and implement data science solutions.
4) Certification
Offer a certification exam to validate students' understanding of Power BI Automated Machine Learning.
Provide preparation materials and resources to help students succeed.
5) Continuous Learning
Encourage students to stay updated with the latest advancements in data science and machine learning.
Provide access to additional learning materials and opportunities for further development.
Strategies to Increase Points in Various Contexts:
Academic:
- Attend class regularly and participate actively: Engagement and attendance earn participation points.
- Complete all assignments on time and to a high standard:* Ensure quality and thoroughness.
- Seek extra credit opportunities:* Offer to assist the instructor or engage in optional projects.
- Review material thoroughly:* Dedicate time to studying and understanding concepts.
- Form study groups:* Collaborate with peers to enhance understanding and share perspectives.
- Sports:
- Practice consistently and diligently:* Improve skills and enhance performance.
- Follow the coach's instructions:* Execute plays and strategies effectively.
- Maintain a positive attitude:* Encourage teammates and display sportsmanship.
- Stay fit and healthy:* Physical conditioning contributes to better gameplay.
- Study the opponents and strategize:* Analyze tendencies and adjust tactics accordingly.
- Games and Competitions:
- Read the rules carefully:* Understand the scoring system and gameplay mechanics.
- Stay focused and avoid distractions:* Concentrate on the task at hand.
- Use logical reasoning and critical thinking:* Devise winning strategies.
- Practice and improve your skills:* Engage in training or practice games.
- Seek assistance or mentorship:* Learn from experienced players or coaches.
- Loyalty Programs:
- Make frequent purchases:* Accumulate points faster by engaging with the brand.
- Sign up for exclusive offers and promotions:* Earn bonus points or discounts.
- Refer friends or family:* Encourage others to join the program and earn referral points.
- Utilize the program's app or website:* Track your progress, redeem rewards, and access additional opportunities.
- Participate in surveys or provide feedback:* Provide valuable input and earn points.
- Social Media:
- Create engaging and relevant content:* Share valuable information or entertainment.
- Interact with followers:* Respond to comments, messages, and inquiries promptly.
- Run contests or giveaways:* Encourage participation and attract new followers.
- Cross promote on multiple platforms:* Extend your reach and increase visibility.
- Collaborate with influencers or brands:* Partner with others to gain access to their audience and earn points.
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Roshan Chaturvedi
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