Math for Machine Learning: Series 3 — Probability & Statistics

📰 Medium · Machine Learning

Learn the mathematical foundations of machine learning, focusing on probability and statistics, to improve your skills in this field

intermediate Published 18 Jun 2026

Action Steps

Apply Bayes' Theorem to update probabilities based on new data
Calculate maximum likelihood to estimate model parameters
Use probability distributions to model uncertainty in machine learning
Analyze data using statistical methods to inform model development
Implement probabilistic models using popular libraries like TensorFlow or PyTorch

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this knowledge to build more accurate models and make informed decisions

Key Insight

💡 Understanding probability and statistics is crucial for building accurate machine learning models

Key Takeaways

Learn the mathematical foundations of machine learning, focusing on probability and statistics, to improve your skills in this field

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Title: Math for Machine Learning: Series 3 — Probability & Statistics

URL Source: https://medium.com/@moezbenazzouz/math-for-machine-learning-series-3-probability-statistics-a0f994a7c1e4?source=rss------machine_learning-5

Published Time: 2026-06-18T12:01:01Z

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# Math for Machine Learning: Series 3 — Probability & Statistics | by Moez Ben-Azzouz | Jun, 2026 | Medium

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# Math for Machine Learning: Series 3 — Probability & Statistics

## From Bayes’ Theorem to Maximum Likelihood — The Mathematics of Uncertainty

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If you missed them, here are the first two parts of the curriculum:

* [Series 1](https://medium.com/@moezbenazzouz/math-for-m

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Math for Machine Learning: Series 3 — Probability & Statistics

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