Applied Machine Learning in Python Coursera

Overview

The Applied Machine Learning in Python course is offered by Coursera in partnership with University of Michigan will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled.

Key facts

• Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling 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 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.

What Will You Learn

• Describe how machine learning is different than descriptive statistics

• Create and evaluate data clusters

• Explain different approaches for creating predictive models

• Build features that meet analysis needs

Skills you'll gain

• Algorithms

• Machine Learning

• Machine Learning Algorithms

• Python Programming

• Applied Machine Learning

• Data Analysis

• Regression

• Human Learning

• Statistical Programming

• Computer Programming