Machine Learning Models in Power BI
Develop AutoML and ML Solutions with Power BI
Development ,Data Science,Machine Learning
Lectures -24
Resources -5
Duration -3 hours
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Course Description
Machine Learning Models in Power BI
Develop AutoML and Manual Machine Learning Solutions with Power BI
Course Overview:
This comprehensive course aims to introduce participants to the intricacies of integrating Machine Learning models within Power BI. It provides a balanced approach, focusing on both automated machine learning (AutoML) solutions and manual model development from scratch.
Agenda:
- Data Wrangling
- Fundamental concepts and techniques
- Unsupervised Machine Learning (Clustering)
- Introduction to clustering methods
- AutoML solutions for clustering
- Developing clustering models from scratch
- Dashboard development for clustering insights
- Supervised Machine Learning
- Introduction to regression and classification methods
- AutoML solutions for regression and classification
- Developing regression and classification models from scratch
- Dashboard development for supervised learning insights
Key Learning Outcomes:
- Understand the difference between AutoML and manual machine learning approaches.
- Acquire skills in various machine learning techniques.
- Engage in hands-on Python code development for machine learning.
- Master model development, training, and optimization.
- Enhance your data visualization skills within Power BI.
- Learn the art of feature selection to improve model accuracy.
- Work on three distinct dashboard projects to showcase your newfound expertise.
Goals
The primary goal of this course is to empower participants with the skills and knowledge to integrate Machine Learning models within Power BI. The course focuses on both automated (AutoML) and manual machine learning solutions, ensuring participants gain a comprehensive understanding of the subject.
Prerequisites
Python Installation and Power BI Desktop

Curriculum
Check out the detailed breakdown of what’s inside the course
Course Intro and Data Wrangling 101
5 Lectures
-
Agenda 02:21 02:21
-
Data Wrangling Part I - Functions (Read CSV, Merge, and Drop) 04:57 04:57
-
Data Wrangling Part II - Functions (Fillna, Unique, Replace, and Value Counts) 06:49 06:49
-
Data Wrangling Part III - Functions ( Grouping, Pivot, Transform ) 06:58 06:58
-
Data Wrangling Part IV - Conditional Logic and User Defined Functions 05:21 05:21
Clustering Models
6 Lectures

Regression Models
9 Lectures

Classification Models
4 Lectures

Instructor Details

Gaelim Holland
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