Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Train Machine Learning Models

CertNexus via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

1 day 4 hours 39 minutes
Sessions

Self-Paced
Level

Intermediate
Subtitles

English, Arabic, French, Portuguese, German, Thai, Indonesian, Spanish, Kazakh, Hindi, Russian, Swedish, Korean, Greek, Italian, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish

Found in

Part of

CertNexus Certified Data Science Practitioner

Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

Unlock All Certificates

This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems. To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.

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.