Applied Machine Learning with Python Literacy (Beginner Level)

  • 19m
  • 19 questions
The Applied Machine Learning with Python Literacy benchmark will measure your ability to identify machine learning algorithms and models, and principles behind building these systems. A learner who scores high on this benchmark demonstrates that they have a basic understanding of machine learning fundamentals.

Topics covered

  • describe how regression works by finding the best fit straight line to model the relationships in your data
  • describe hyperparameter and the different types of hyperparameter tuning methods
  • describe the approaches and steps involved in developing machine learning models
  • describe the process involved in learning a relationship between input and output during the training phase of machine learning
  • distinguish between supervised learning techniques such as regression and classification, and unsupervised learning methods such as clustering
  • evaluate a regression model using metrics such as r-square and mean squared error and visualize its performance using Matplotlib
  • identify the different types of machine learning models
  • list machine learning metrics that can be used to evaluate machine learning algorithms
  • list machine learning models that can be used to manage classification and regression problems
  • list the characteristics of regression such as simplicity and versatility, which have led to the widespread adoption of this technique in a number of different fields
  • load data from a CSV file into a Pandas dataframe and prepare the data for training a classification model
  • recognize the application of a confusion matrix and how it can be used to measure the accuracy, precision, and recall of a classification model
  • recognize the differences between machine learning models and algorithms
  • recognize the different kinds of machine learning algorithms such as regression, classification, and clustering, as well as their specific applications
  • recognize the problems associated with a model that is overfitted to training data and how to mitigate the issue
  • use Pandas and Seaborn to visualize the correlated fields in a dataset
  • use Pandas for feature extraction and one-hot encoding, load its contents into a ModelFrame, and initialize and train a linear regression model
  • use Pandas ML to explore a dataset where the samples are not evenly distributed across the target classes
  • use the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functions