Aspire Journeys

Improving Machine Learning Models

  • 8 Courses | 1h 20m
  • 5 Labs | 5h
Machine Learning models are at the heart of many of the digital applications we use everyday. This Aspire Journey walks through how to improve machine learning models to make them performant, effective and generalizable, using techniques like feature selection, regularization, hyperparameter tuning, ensembling and building pipelines.

Track 1: Feature Engineering

In this track of the Machine Learning Aspire Journey, the focus will be on mastering the feature engineering techniques such as regularization, filter and wrapper methods, dimensionality reduction and hyperparameter tuning.

  • 5 Courses | 50m
  • 2 Labs | 2h

Track 2: Ensembling Methods

In this track of the Machine Learning Aspire Journey, the focus will be on mastering the feature engineering techniques such as regularization, filter and wrapper methods, dimensionality reduction and hyperparameter tuning.

  • 2 Courses | 20m
  • 2 Labs | 2h

Track 3: Machine Learning Pipelines

In this track of the Machine Learning Aspire Journey, the focus will be on mastering machine learning workflows and how to build a machine learning pipeline using `scikit-learn`.

  • 1 Course | 10m
  • 1 Lab | 1h

EARN A DIGITAL BADGE WHEN YOU COMPLETE THESE TRACKS

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

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