Description for Data Science in Python : Introduction
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Michigan
Duration: 34 hours (approximately)
Schedule: Flexible
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In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
This coursework provides an extensive introduction to Python programming, data manipulation techniques, and fundamental development tools necessary for proficient coding practices.
Acquire the fundamental skills of data management, extraction, querying, and visualization to power your AI initiatives.
For the purpose of accounting data analytics, the course educates students on the application and optimization of machine learning models in Python.
Examine the development and deployment of interactive Python data applications, with a particular emphasis on Recommender Systems and the use of Python web frameworks to deploy and monitor machine learning models.
Using Python, participants will analyze supply chain datasets, resolve optimization issues, and cultivate transferable data analysis abilities.
This course provides practical experience with machine learning through case studies, concentrating on applying approaches across domains and laying the groundwork for deeper understanding of models and algorithms.
This course offers a structured Python introduction for individuals who are not majoring in computer science. The course concentrates on data analysis and visualization, with practical, cross-disciplinary applications.
Master the process of exploratory data analysis, train AutoML models with Vertex AI and BigQuery ML, optimize models using performance metrics and loss functions, and generate scalable datasets for training and evaluation.
The course encompasses the following topics: the development of a text processing pipeline, the comprehension of Naive Bayes classifier theory, and the assessment of the efficacy of classification models following training.
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