Applied Machine Learning with Python Competency (Intermediate Level)

  • 23m
  • 23 questions
The Applied Machine Learning with Python Competency benchmark will measure your ability to identify and apply machine learning algorithms to build learning systems. A learner who scores high on this benchmark demonstrates that they have the machine learning skills necessary to model data and build learning systems.

Topics covered

  • build machine learning pipelines
  • combine the use of oversampling and PCA in building a classification model
  • compare training and evaluation in Pandas ML with the equivalent tasks in scikit-learn
  • configure and build a clustering model using the K-Means algorithm and analyze data clusters to determine characteristics that are unique to them
  • create and save machine learning models using scikit-learn
  • create machine learning models in production
  • demonstrate how to tune hyperparameters using grid search
  • deploy machine or deep learning models in production
  • describe how clustering algorithms are able to find data points containing common attributes and thus create logical groupings of data
  • evaluate a regression model using metrics such as r-square and mean squared error and visualize its performance using Matplotlib
  • identify the benefits of combining Pandas, scikit-learn, and XGBoost into a single library to ease the task of building and evaluating ML models
  • implement machine learning pipelines using scikit-learn
  • install Pandas ML and then define and configure a ModelFrame
  • list prominent tools that can be used to build machine learning pipelines
  • perform undersampling operations on a dataset by applying the Near Miss, Cluster Centroids, and Neighborhood Cleaning Rule techniques
  • recall the steps involved in iterative machine learning model management and the associated benefits
  • recognize the essential aspects of a reproducible study
  • recognize the need to reduce large datasets with many features into a handful of principal components using the PCA technique
  • set up machine learning models in production using Flask
  • train and evaluate a classification model to predict the quality ratings of red wines
  • transform a dataset containing multiple features to a handful of principal components and build a classification model using the reduced dimensions of the dataset
  • use the EasyEnsembleClassifier and BalancedRandomForestClassifier available in the imbalanced-learn library to build classification models with imbalanced data
  • work with ModelFrames for feature extraction and label encoding