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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: 1,608
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
COURSE
Intermediate
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.
EXAM
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
COURSE
Intermediate
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.
EXAM
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
HANDS-ON LAB
Beginner
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.
COURSE
Intermediate
Duration: 1 hour and 1 minute
PyTorch 101
This lesson introduces you to PyTorch and focuses on two main concepts: PyTorch tensors and the autograd module.
HANDS-ON LAB
Beginner
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.
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