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Practical Machine Learning: Foundations to Neural Networks

You will develop the ability to rigorously formulate learning tasks using probability and statistics, distinguish Bayesian and frequentist perspectives, build linear models for regression and classification, estimate optimal model parameters via Maximum Likelihood Estimation (MLE), and apply neural networks to practical problems. The series progresses from foundational methods to real-world neural network implementation. By the end of this specialization, learners will be able to: Express learning tasks with mathematical rigor using ideas from probability and statistics. Deconstruct Bayesian and frequentist perspectives and utilize these perspectives to approach machine learning tasks with well-reasoned strategies. Apply maximum likelihood estimate (MLE) to find optimal parameters of a model. Build linear models for regression and for classification. Design and implement artificial neural networks tailored to the needs of particular regression and classification tasks.Apply the theory of neural networks to building models.

Status: Artificial Intelligence

Status: Predictive Modeling

IntermediateSpecialization

Top reviews across Practical Machine Learning: Foundations to Neural Networks

5.0

Reviewed Sep 18, 2026

Course: Machine Learning Fundamentals

This course is for true learners. It helped me follow Bishop's rigorous textbook "Pattern Recognition and Machine Learning" and also gave opportunity to code theory in NumPy.

Learner reviews across Practical Machine Learning: Foundations to Neural Networks

Showing: 2 of 2

Romel

Course: Foundations for Machine Learning

5.0

Reviewed Dec 14, 2025Course: Foundations for Machine Learning

This is a very well-made course. Labs and quizzes do a great job at reinforcing concepts covered. They provide further explanations via feedback. Additionally, most assignments are not simple "plug x value into the equation" type of questions and problems. Instead, they generally encourage some greater thoughts. Some labs are problem statements followed by a blank canvas. Subsequent quizzes are to ensure your implementations output the anticipated answers. I do not think you should jump into this course without the "Recommended experience," however. Peter walks through proofs and derivations, and if you don't have the expected mathematical background, you will have a hard time following along.

Ahmed

Course: Foundations for Machine Learning

5.0

Reviewed Dec 10, 2025Course: Foundations for Machine Learning

Professor Peter Chin is an exceptional educator. I’ve been using advanced machine learning software in my work for a few years, but never had the time to understand what happens behind the scenes. In just a few days, his course made it clear what’s really going on when these models execute my commands — all explained intuitively and grounded in solid scientific principles.