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.
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课程信息
Understand data science concepts, experience with programming languages (Python), libraries (NumPy,pandas) and database querying languages (SQL).
您将获得的技能
- design of experiments
- Machine Learning
- clustering
- regression
- classification
Understand data science concepts, experience with programming languages (Python), libraries (NumPy,pandas) and database querying languages (SQL).
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CertNexus
CertNexus is a vendor-neutral certification body, providing emerging technology certifications and micro-credentials for Business, Data, Development, IT, and Security professionals. CertNexus’ exams meet the most rigorous development standards possible which outlines a global framework for developing personnel certification programs to narrow the widening skills gap.
授课大纲 - 您将从这门课程中学到什么
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.
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