Applied Machine Learning with Python Competency (Intermediate Level)

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