Build & practice supervised machine learning products for prediction & binary classification responsibilities, such as linear regression & logistic regression
By the end of this Specialization, you should have mastered vital ideas and obtained the sensible know-ways to swiftly and powerfully implement machine learning to hard genuine-world challenges.
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• Use most effective procedures for machine learning development so that your models generalize to details and duties in the true world.
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Before the graded programming assignments, there are actually further ungraded code notebooks with sample code and interactive graphs to help you visualize what an algorithm is executing and enable it to be less complicated to accomplish programming workout routines.
The section on realistic guidance on making use of machine learning has been up to date appreciably depending on emerging read more ideal tactics from the last decade.
Create recommender units that has a collaborative filtering technique as well as a content material-centered deep learning system
• Use unsupervised learning approaches for unsupervised learning: like clustering and anomaly detection.
• Establish recommender programs with a collaborative filtering strategy in addition to a material-dependent deep learning technique.
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"I directly utilized the concepts and techniques I uncovered from my courses to an enjoyable new undertaking at work."
These lessons are optional and so are not required to complete the Specialization or implement machine learning to real-entire world projects.
• Create and practice supervised machine learning types for prediction and binary classification responsibilities, which includes linear regression and logistic regression.