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How does a Machine Learning project actually work?
How does a Machine Learning project actually work?
Last week, we explored how machines learn from data, including models, training, and predictions.
This week, we're looking at how all these pieces come together in a real Machine Learning project.
Where do you start? What happens to the data? And how do you know if your model actually works?
Week 4 of ML From First Principles, in plain language:
📌 Defining the problem
📌 Collecting data
📌 Preparing data
📌 Training the model
📌 Evaluating the model
📌 Making predictions
No jargon overload. Just clear ideas, simple examples, and a picture of how it all connects.
Because Machine Learning isn't just about choosing an algorithm and pressing train.
It's a process that starts with understanding the problem, depends on good data, and requires evaluation to check whether the model performs well on data it hasn't seen before.
Join me as we continue this journey, one concept at a time.
#MachineLearning #ML #LearningInPublic #Python #SoftwareEngineering
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