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    SpicyMags.xyz

    Complete Power Bi Course With Augmented Analytics & Auto Ml

    Posted By: ELK1nG
    Complete Power Bi Course With Augmented Analytics & Auto Ml

    Complete Power Bi Course With Augmented Analytics & Auto Ml
    Last updated 12/2022
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 2.43 GB | Duration: 7h 0m

    Data Transformation | DAX | Data Models | Simple, Complex & AI Visuals | Forecasting, Anomaly & Sentiment Analytics

    What you'll learn

    Power BI - Basic & Advanced

    How to use DAX

    How to create and publish visuals using Power BI

    Machine Learning Concepts

    How to build AI - ML Models without writing a single line of code

    How to transform data

    How to build a word cloud without any coding

    What is autoML

    How to join tables

    How to identify anomalies/outliers in your dataset

    How to restrict the access of your reports and dashboards

    Requirements

    None.

    Knowledge of excel would be advantageous

    Description

    Recent Updates:Aug 2022: Added a video lecture on Clustering and SegmentationJuly 2022: Updated the course with using DAX functions like Calculate and LookupJune 2022: Added a case study on using Python (programming language) in PowerBI environmentMay 2022: Added a case study on Benford Law (Benford law is used to detect fraud)April 2022: Added a case study on Ageing Analysis–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Course Description:In the last 50 years, the world of reporting, analytics and business intelligence (BI) has seen many of evolutions. The notable ones are the rise of self service BI and augmented analytics. Businesses are no longer content with descriptive or diagnostic analytics. The expectation for prescriptive and predictive analytics has become the new normal. Machine learning and Artificial Intelligence technology has also evolved significantly in the last decade and the notable evolution is the rise of Auto ML - the no code machine learning approaches. Auto ML has significantly democratized predictive analytics. End users can predict the future outcomes of businesses in a few (mouse) clicks.Power BI epitomizes the recent trends in business intelligence (BI) and augmented analytics for interactive, easy to use and self-serve dashboards & auto ML capabilities.This is a comprehensive course on Power BI covering the following:Data TransformationDAXData ModelsSimple & complex visualsAI enabled visualsAuto ML: Concepts and no code approaches to forecast futureThere are no pre-requisites for this course, although knowledge of excel would be advantageous.There are actually 2 courses (Power BI and AI - ML) in this course and both are covered in great detail. Whether your objective is to learn power bi or machine learning or both, this course will deliver the goods for you.

    Overview

    Section 1: Introduction

    Lecture 1 Introduction

    Lecture 2 Descriptive Vs Diagnostics Vs Prescriptive Vs Predictive Analytics

    Lecture 3 Traditional BI Vs Self Service BI Vs Augmented BI

    Lecture 4 PowerBI

    Lecture 5 What is Auto ML

    Lecture 6 Assignment

    Section 2: PowerBI Download and Understanding the tool

    Lecture 7 Power BI Download

    Lecture 8 Introducing the Power BI tool

    Section 3: Loading and Transforming data

    Lecture 9 Loading the data

    Lecture 10 Data Transformation 1

    Lecture 11 Data Transformation 2: Null, Merging columns, Extracting info, Groupby

    Lecture 12 Data Transformation 3: Null, Pivot & Unpivot

    Section 4: Visualization in PowerBI

    Lecture 13 Text Box

    Lecture 14 Card Visual

    Lecture 15 Stacked Column Chart

    Lecture 16 Stacked Bar Chart

    Lecture 17 Multi Row Card

    Lecture 18 Tree Map

    Lecture 19 Pie Chart

    Lecture 20 Dual Axis Chart (Line and Stacked Column Chart)

    Lecture 21 Ribbon Chart

    Lecture 22 Slicer

    Lecture 23 Map Visual

    Lecture 24 Page Tool Tip

    Lecture 25 Funnel Chart

    Section 5: DAX

    Lecture 26 Introduction to DAX

    Lecture 27 Understanding different types of DAX functions

    Lecture 28 Gage Chart

    Lecture 29 Conditional Column

    Lecture 30 Format Date Columns and Use of Max

    Lecture 31 Year to date (YTD)

    Lecture 32 Calculate

    Lecture 33 Lookup

    Section 6: AI Enabled Visuals

    Lecture 34 What If Analysis

    Lecture 35 Key Influencers

    Lecture 36 Q and A

    Section 7: Tables: Relationships and Joining Tables

    Lecture 37 Introduction and 1 to 1 Relationships

    Lecture 38 1 to Many Relationships

    Lecture 39 Different types of Join

    Section 8: Publishing the report

    Lecture 40 Publishing the report

    Section 9: Row Level Security

    Lecture 41 Row Level Security

    Section 10: Case Studies

    Lecture 42 Benford Law (Useful in fraud detection)

    Lecture 43 Ageing Analysis

    Lecture 44 Using Python in PowerBI environment and creating conditional scatter plots

    Section 11: AI Concepts

    Lecture 45 Dependent Vs Independent Variable

    Lecture 46 ML Concepts

    Lecture 47 Accuracy in Regression and Classification

    Lecture 48 Regression and Classification Concepts

    Section 12: Regression and Classification in Power BI

    Lecture 49 Simple Linear Regression in PowerBI

    Lecture 50 Multiple Linear Regression in Power BI

    Lecture 51 Classification in Power BI

    Section 13: Time Series Forecasting and Anomaly Detection Using Power BI

    Lecture 52 Forecasting and Anomalies Concepts

    Lecture 53 Time Series Forecasting Using Power BI

    Lecture 54 Anomaly Detection Using Power BI

    Section 14: NLP

    Lecture 55 What is NLP (Natural Language Processing)

    Lecture 56 NLP Concepts

    Lecture 57 Wordcloud Using Power BI

    Section 15: Clusters & Magic Quadrant in PowerBI: Unsupervised Learning: Customer Segments

    Lecture 58 Clusters & Magic Quadrant in PowerBI: Unsupervised Learning: Customer Segments

    Students,Experienced professionals,Those interested in building Business Intelligence Visuals & Dashboards,Machine Learning enthusiasts,Those who are curious whether it is possible to build ML model without coding,Executives who want to learn AI in more detail but don't want to do coding