Applied Machine Learning in Python
Applied Machine Learning in Python
This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
Coursera, partnered with the University of Michigan, has an Applied Data Science with Python Specialization, an impressive course to learn data science. This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods.
The course will discuss how machine learning is different from descriptive statistics and introduce the scikit learn toolkit through a tutorial. The issue of the dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described. Learners will be able to apply the scikit learn predictive modeling methods while understanding process issues related to data generalizability (e.g., cross-validation, overfitting).
The course will end with a look at more advanced techniques, such as building ensembles 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 unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python, and before Applied Text Mining in Python and Applied Social Analysis in Python.
Course 3 of Applied Data Science with Python Specialization
Institution: University of Michigan, via Coursera
