Advanced Python in Excel: Machine Learning

36mAdvanced2024-09-12

Authors

Christian Martinez

Course details

Demand is increasing for data-driven decision-making and the rapid integration of Python in Excel and ML across business sectors. In this course, Finance Transformation Senior Manager Christian Martinez offers timely training that empowers you to efficiently leverage vast amounts of data for competitive advantage, aligning with current market needs.

Learning objectives
Gain proficiency in integrating Python with Excel to implement machine learning models directly within a familiar spreadsheet environment.
Explore practical applications of machine learning in business and finance, such as predictive analytics and automated decision-making processes.
Develop skills in using libraries like pandas, NumPy, and scikit-learn within Excel, enhancing the analytical capabilities of business data processing.
Learn how to use machine learning algorithms like linear regression, random forests, and clustering techniques for business and finance.

Skills covered

Machine LearningSpreadsheetsMicrosoft ExcelPythonArtificial Intelligence (AI)Programming LanguagesBusiness Software and ToolsOpen SourceMicrosoftSoftware DevelopmentOne-Off

Concepts

0. Introduction

01 - Introduction
02 - What you should know

1. Recap of Python, ML, and Data Cleaning

03 - Recap of Python
04 - Recap of machine learning
05 - Data cleaning and preparation
06 - Challenge - How would you solve this problem with ML
07 - Solution - How would you solve this problem with ML

2. Applied Machine Learning for Finance and Business

08 - Introduction to machine learning concepts
09 - Building regression models in Excel
10 - Classification models for business data
11 - Challenge - Build a regression model
12 - Solution - Build a regression model

3. Practical Applications of Machine Learning in Business and Finance

13 - Predictive analytics for financial forecasting
14 - Automated decision-making processes
15 - Real-time data processing and analysis
16 - Challenge - Create a financial forecast
17 - Solution - Create a financial forecast

4. Advanced Analytical Techniques with Python Libraries

18 - Data manipulation with pandas
19 - Numerical analysis with NumPy
20 - Machine learning with scikit-learn

5. Machine Learning Algorithms for Business and Finance

21 - Linear regression for business insights
22 - Random forests for predictive modelling
23 - Clustering techniques for market segmentation

Conclusion

24 - Next steps