What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
Article
This course is part of CertNexus Certified Data Science Practitioner Professional Certificate
Instructors: Stacey McBrine
Included with
Recommended experience
Intermediate level
Understand data science concepts, experience with programming languages (Python), libraries (NumPy,pandas) and database querying languages (SQL).
Recommended experience
Intermediate level
Understand data science concepts, experience with programming languages (Python), libraries (NumPy,pandas) and database querying languages (SQL).
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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.
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.
15 videos6 readings1 assignment2 peer reviews1 discussion prompt
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.
18 videos9 readings1 assignment1 discussion prompt7 ungraded labs
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.
13 videos7 readings1 assignment1 discussion prompt4 ungraded labs
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.
9 videos5 readings1 assignment1 discussion prompt4 ungraded labs
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.
1 peer review1 ungraded lab
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