This course was ranked in the Top 100 Best Courses on Coursera, based on its high ratings and large number of ratings.
This course will introduce the student to applied machine learning, focusing more on the techniques and methods than the statistics behind these methods. The course will start with a discussion of how machine learning is different from descriptive statistics and introduce the scikit learn toolkit through a tutorial. The issue of data dimensionality will be discussed and the task of grouping data will be addressed, as well as how to evaluate those groups. Supervised approaches to creating
This course will introduce the student to applied machine learning, focusing more on the techniques and methods than the statistics behind these methods.
The course will start with a discussion of how machine learning is different from descriptive statistics and introduce the scikit learn toolkit through a tutorial.
The issue of data dimensionality will be discussed and the task of pooling data will be addressed, as well as how to evaluate those pools.
Supervised approaches to building predictive models will be described and students will be able to apply scikit learn predictive modeling methods while understanding process issues related to data generalization (eg, cross-validation, overfitting).
The course will end with a look at more advanced techniques, such as ensemble construction, and the practical limitations of predictive models.
By the end of this course, students will be able to identify the difference between a supervised (classification) and an unsupervised (clustering) technique, identify which technique they need to apply for a particular data set and need, design features to meet that need, and write Python code to perform an analysis.
This course should be taken after Introduction to Data Science in Python and Applied Data Plotting, Graphing, and Representation in Python and before Applied Text Mining in Python and Applied Social Analytics in Python.
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This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
This module delves into a wider variety of supervised learning methods for classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity using model complexity. application of techniques such as regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
This module covers model evaluation and selection methods that you can use to help understand and optimize the performance of your machine learning models.
This module covers more advanced supervised learning methods including ensemble trees (random forests, gradient-powered trees) and neural networks (with an optional overview on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
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