In this course, you’ll learn how to develop a machine learning workflow for classification tasks using scikit-learn. You’ll learn how to build and implement the k-nearest neighbors algorithm using pandas and scikit-learn. Finally, you’ll learn to train, validate, and improve your machine learning model for better performance and accuracy using techniques like tuning hyperparameters.
Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll combine your new skills to complete a project to predict heart disease.
- Establishing a machine learning workflow
- Implementing the k-nearest neighbors algorithm for a classification task using pandas
- Implementing the k-nearest neighbors algorithm using scikit-learn
- Improving model performance using hyperparameters
Introduction to Supervised Machine Learning in Python [5 lessons]
- Define machine learning and supervised machine learning
- Establish a machine learning workflow
- Define classification and different types of classification tasks
- Build a linear SVM classifier (LinearSVC) using scikit-learn
- Compare the balance between applying an ML model with and without understanding the algorithm
- Explain how the K-Nearest Neighbors algorithm works for classification tasks
- Define the role of distance metrics for the K-Nearest Neighbors algorithm
- Implement the K-Nearest Neighbors algorithm for one feature using pandas
- Implement the K-Nearest Neighbors algorithm for multiple features using pandas
- Engineer new features to improve model performance
- Evaluate the model using accuracy as the metric
- Conduct exploratory data analysis on the dataset
- Clean and prepare the dataset for training a k-NN model
- Identify features of interest for training the model
- Build and train a classifier using selected features
- Find optimal hyperparameter values using grid search
- Evaluate the model's performance on the test set
Projects in this course
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