Practical Machine Learning: Foundations to Neural Networks
About this course
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.
83/100
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- What the provider tells you
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- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Express learning tasks with mathematical rigor using ideas from probability and statistics.
- Distinguish between Bayesian and frequentist perspectives in machine learning.
- Apply maximum likelihood estimation to find optimal model parameters.
- Construct linear models for regression and classification.
- Design and implement artificial neural networks for specific tasks.
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