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Build on your foundational knowledge of Machine Learning and develop solutions for complex business problems using the capability of machine learning. Learn advanced concepts and understand them by practicing your learning in hands-on exercises. Create machine learning models and explore the right methods to evaluate such models.
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Course instructors and designers from top businesses including Google, Amazon, Twitter and IBM.
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Curriculum primed for industry relevance and developed with guidance from industry advisory boards.
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Learn how to use linear algebra in creating effective machine learning algorithms.
Familiarize yourself with the Calculus techniques used in machine learning.
Master the foundational concepts in machine learning and how to apply them wisely.
Learn to use linear regression to make a machine learning model for predictive analysis.
Leverage Lasso regression to build more accurate prediction models with Python.
Learn how and when to use ridge regression and how it produces better results.
Learn to combine the Lasso and Ridge regression in using the Elastic-Net regression.
Use logistic regression by using classification algorithm to make yes or no decisions.
Learning Objective: Learn how to use linear algebra to represent and manipulate data and in creating mathematical models.
Learning Objective: Learn how you can use calculus to understand and optimize machine learning algorithms.
Learning Objective: Understand the difference between statistical learning and machine learning and how ML approaches use statistical functions under the hood to find good models. Learn important machine learning concepts such as overfitting and cross-validation for overfitting detection and model evaluation. Additionally, understand bias, variance and the bias-variance tradeoffs in the context of machine learning models.
Learning Objective: Cover the simplest form of statistical learning: linear regression, including simple linear regression and multiple linear regression. Understand topics of covariance, correlations and method of least squares. Significance of parameters, methods to assess fit and assumptions about the data are covered to understand the results and validity of applying linear regression.
Learning Objective: Understand the theory of classification in statistical learning and the logistic regression model. Learn about the technique of maximum likelihood estimation. Diagnostic methods, metrics, and techniques to deal with imbalanced datasets are also covered in this module. A practical example combining all the mentioned is included in this section.
