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Machine Learning with Python

This course explores a range of techniques for building machine learning pipelines with Python.

Difficulty: Intermediate

Duration: Up to 9 hours and 38 minutes

Students: 1,726

Rating: 5/5

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10 steps

There are 3 Courses in this learning path

There are 2 Exams in this learning path

There are 5 Hands-on Labs in this learning path

Duration: 53 minutes and 23 seconds

Building Machine Learning Pipelines with scikit-learn - Part One

This lesson is the first in a two-part series that covers how to build machine learning pipelines using scikit-learn, a library for the Python programming language.

Duration: Up to 30 minutes

Knowledge Check: Building Machine Learning Pipelines with scikit-learn - Part One

Knowledge Check: Building Machine Learning Pipelines with scikit-learn - Part One

Duration: 1 hour and 9 minutes

Building Machine Learning Pipelines with scikit-learn - Part Two

This lesson is the first in a two-part series that covers how to build machine learning pipelines using scikit-learn, a library for the Python programming language.

Duration: Up to 35 minutes

Knowledge Check: Building Machine Learning Pipelines with scikit-learn - Part Two

Knowledge Check: Building Machine Learning Pipelines with scikit-learn - Part Two

Duration: Up to 1 hour

Machine Learning with scikit-learn

The aim of this lab is to challenge you on building a supervised machine learning pipeline to predict the median values of owner-occupied housing in USD 1000 in the Boston dataset.

Duration: 1 hour and 1 minute

This lesson introduces you to PyTorch and focuses on two main concepts: PyTorch tensors and the autograd module.

Duration: Up to 1 hour

PySpark - Preprocessing

In this lab, you will learn how to create a dataset using the PySpark library, and to manipulate it using standard filtering and slicing techniques.