Train Machine Learning Models

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Overview

This course focuses on the practical steps to train effective machine learning models across various problem domains. You will learn data preprocessing, feature engineering, model selection, and evaluation techniques. The curriculum covers supervised and unsupervised learning algorithms including regression, classification, clustering, and ensemble methods. Emphasis is placed on tuning hyperparameters, preventing overfitting, and using cross-validation to ensure generalization. The course also covers deployment considerations and monitoring model performance in production. Hands-on projects help solidify concepts by building models on real...

Syllabus

Prepare to Train a Machine Learning Model

In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.

Develop Classification Models

The first type of machine learning task you'll build models for is classification. Classification has many applications across many different fields, so it's a good starting point. In this module, you'll train classification models, tune those models, and then evaluate them as part of a process of iterative improvement.

Develop Regression Models

The next major machine learning task you'll undertake is regression. Whereas classification is about placing things in categories, regression is about estimating numbers. As with the previous module, in this module you'll train, tune, and then evaluate models that perform regression.

Develop Clustering Models

You've built supervised learning models using both classification and regression. But now it's time to work with unsupervised learning, where labeled data is not readily available. In this module, you'll implement unsupervised learning in the form of clustering models, which can group observations that share common traits. Just like before, you'll develop these models as a process of training, tuning, and evaluation.

Apply What You've Learned

You have developed models for classification, regression and clustering, in this module you will apply what you have learned working within a practical scenario. Using a Jupyter notebook you will perform machine learning tasks. You are given the choice of three notebooks, each of which leverages a different type of algorithm.