Presentation of Machine Learning in Power BI with R and Python

Table of Content Presentation: Machine Learning in Power BI with R and Python Authors See Recording Pablo Moreno Gabriel Gomez

With R and Python… Traditional usage of Power BI

Authors Gabriel Gomez Pablo Moreno IT and Systems engineer. Expert in business Intelligence and Senior Data Scientist with software Development. Data experience in business base Manager and Administrator, Intelligence, advanced Analytics, custom reporting and advanced Machine Learning and Artificial Analytics. Intelligence applications for business and Finance.

Reasons for this book It doesn’t exist a similar resource Increasing users of Power BI Users willing to learn more … and because we love it! Provide book content feedback at tiny.cc/l08utz

Section I – Fundamentals Section I I.1 Objectives Section II I.2 Fundamentals about Artificial Intelligence Section III I.3 Data Professionals Section IV I.4 Big Data / Small data I.5 Data Quality I.6 Tidy data I.7 What is Machine Learning I.8 Installing R into Power BI I.9 Installing Python into Power BI I.10 Considerations when working with R / Python into Power BI This index is preliminary and it may change Provide book content feedback at tiny.cc/l08utz

Section II – Advanced Analytics with R Section I II.1 Importing data into Power BI with R Section II II.2 Data wrangling Section III II.3 Data visualization with R Section IV II.4 Text analytics II.5 Advanced Analytics This index is preliminary and it may change II.6 Forecasting with prophet (visualization) II.7 Treating nulls and NaN, imputations II.8 Text editing and regex usage II.9 Adding custom columns II.10 Apply functions to all dataset II.11 Time series forecasting II.13 Lineal regression II.14 Logistic regression II.15 Predictive model in production into Power BI Provide book content feedback at tiny.cc/l08utz

Section III – Machine Learning with Python Section I III.1 Creating a virtual environment in Python for Power BI Section II III.2 Machine Learning with Python Section III III.3 Clustering analysis Section IV III.4 Association rule, recommendation, market-basket analysis III.5 Anomaly detection III.6 Regression analysis III.7 Classification analysis III.8 Text analysis and Natural Language Processing III.9 Sentiment analysis III.10 Predictive model in production into Power BI III.11 Ensemble modelling This index is preliminary and it may change Provide book content feedback at tiny.cc/l08utz

Section IV – Apendix and resources Section I IV.1 Algorithms and models Section II IV.2 Model evaluation Section III IV.3 Glossary Section IV IV.4 Tidyverse documentation This index is preliminary and it may change Provide book content feedback at tiny.cc/l08utz

Book release – Project Plan Feb - May Book Development and writting June External editing and checking July - Aug Spanish publication world-wide Sept English editing Oct - Nov English publication world-wide This index is preliminary and it may change Provide book content feedback at tiny.cc/l08utz

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