Theoretical concepts of Machine Learning, using Python’s sklearn library.
This course covers over 27 functions in Python’s machine learning library, sklearn. The functions covered in this course take the student through the entire machine-learning life cycle.
The student will learn the types of learning that are part of sklearn, including supervised, semi-supervised and unsupervised learning.
The student will learn about the types of estimators used in supervised, semi-supervised, and unsupervised learning, including classification and regression.
The student will learn about a variety of supervised learning estimators including linear regression, logistic regression, decision tree, random forrest, naive bayes, support vector machine, k nearest neighbor, and neural network.
The student will learn about sklearn’s three semi-supervised functions to make predictions on classification problems.
the student will learn about some of the estimators used to make predictions on unsupervised learning, including k means, hierarchical and Gaussian methods.
The student will learn about dimensionality reduction and feature selection as a means of reducing the number of features in the dataset.
The student will learn about the different functions in sklearn that carry out preprocessing activities including standardization, normalization, encoding, and imputation.
The student will learn about hyperparameter tuning and how to perform a grid search on the different parameters in the model to help it work at peak optimization.
The student will learn about the goodness of fit tests, including root mean squared error, accuracy score, confusion matrix, and classification report, which tell the user how well the model has performed.
The students will receive additional learning and cover the machine learning life cycle to enable them to initiate how to own a machine learning project using sklearn.
