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There are many different types of machine learning, each with its own level of complexity. See below to explore the different options:

In machine learning, the strength of outcomes relies heavily on data quality and thoughtful design, forming the foundation for accurate and reliable models.

In real-world machine learning, you craft your models to be in the Goldilocks Zone. How you get in the Zone is by trading off: Simplicity vs. Complexity Bias vs. Variance Today's ML mini-lesson covers this in 2 minutes. Stay healthy and happy data sleuthing!

When working with machine learning, it's easy to try them all out without understanding what each model does, and when to use them. In this cheat sheet, you'll find a handy guide describing the most widely used machine learning models, their advantages, disadvantages, and some key use-cases.

This is a great article on what you might want to check out especially if grappling with machine learning and the next steps for your company. https://lnkd.in/dNREZ6TD

why is feature Scaling important in machine learning process: Feature scaling makes sure that all variables in a dataset have similar scales, preventing any single feature from having a disproportionate impact on the analysis. let's take a practical example: if we have two features in a dataset; Height in meters and weight in kilograms, the weight being larger in scale might dominate the analysis. so as we can see, feature scaling ensures that both height and weight contribute more evenly thereby it makes our comparison meaningful

Overfitting vs. Underfitting: Getting the Balance Right in Machine Learning

In this article, Guillaume COLLEY outlines three ways to expand your machine learning feature set with features that represent explainable behaviors and that maximize predictive power:

Making sense of massive datasets is challenging: A recent study by Dew et al. reviews probabilistic machine learning models and inference methods highlighting their utility for addressing common marketing problems. https://ow.ly/B9T250U7ftT #MachineLearning #BigData

Access the full potential of Machine Learning (ML) by combining data from different areas of your business. This gives your model a more well-rounded view of your customers and operations, leading to smarter predictions and decisions.