What you would learn in Applied Machine Learning in Python course?
This course will expose learners to machine learning applied with a focus on methods and techniques rather than the statistical basis that underlie these techniques. The course will begin by discussing how machine learning differs from descriptive statistics. Then, we will introduce the scikit learn toolkit with a demonstration. The question of the dimensionality of data will be addressed, and the issue that involves clustering the data and evaluating these clusters will be addressed. The methods to create prediction models are explained, and students can apply Scikit Learn predictive modeling methods while learning about the process concerns related to the generalizability of data (e.g., cross-validation, overfitting). The course will conclude by examining more advanced techniques like building ensembles and the practical limitations of predictive models. At the end of the course, students will be able to recognize the differences between an unsupervised (classification) and an unsupervised (clustering) technique, decide the method they should apply to a particular data set and the need for it, design features to meet the requirement, and write Python software to conduct an analysis.
This course should be completed following Introduction to Data Science in Python and Applied Plotting Charting and data Representation with Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Course Content:
Explain how machine learning differs from descriptive statistics.
Make and analyze clusters of data.
Define different ways of developing predictive models.
Create features that can satisfy the needs of the analysis
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