What this book covers

Chapter 1, Implementing Machine Learning Algorithms, covers the basic machine learning algorithms and how to implement them.

Chapter 2, Hands-On Examples of Machine Learning Models, adds some examples of algorithms and their use cases.

Chapter 3, Importing Data into Excel from Different Data Sources, covers how to read data from different sources into Excel.

Chapter 4, Data Cleansing and Preliminary Data Analysis, describes data preprocessing to prepare data for use in machine learning models.

Chapter 5, Correlations and the Importance of Variables, covers feature engineering, which involves identifying redundant variables and useful relationships between variables.

Chapter 6, Data Mining Models in Excel Hands-On Examples, describes examples of the most frequently used algorithms in solving business problems such as Market Basket Analysis and customer cohort analysis.

Chapter 7, Implementing Time Series, covers time series analysis and prediction.

Chapter 8, Visualizing Data in Diagrams, Histograms, and Maps, describes the different available diagrams in Excel and what they are used for.

Chapter 9, Artificial Neural Networks, covers advances machine learning in the form of artificial neural networks and deep learning.

Chapter 10, Azure and Excel - Machine Learning in the Cloud, covers building and using machine learning models in the cloud, connecting them to Excel.

Chapter 11, The Future of Machine Learning, covers the automation of data analysis and predictive models.