Machine Learning with Python

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Course Start Date: July 4, 2022
Applications Closed

Machine Learning (ML) is a rapidly growing field that has captured the global community’s interest. Given all the buzz around, it can be challenging to understand what it is. This course will help you grasp what ML is and what it is not and recognize the motivations and use cases related to ML. You will see why Python has become the tool of choice for ML and then use it to solve multiple types of ML problems.

This course features hands-on labs hosted on CENGN’s multi-vendor cloud, using the popular Jupyter Notebook web-based interactive development environment. The course culminates with an end-to-end exercise including data cleaning and visualization, problem specification, algorithm selection and results analysis.

This training package includes one attempt for the CENGN Machine Learning with Python exam. Those who complete the exam will earn a CENGN Machine Learning with Python digital badge, which can be posted on LinkedIn and referenced on your resume.

Machine Learning with Python

Recommended for Students Interested in Becoming:

  • A Software developer/engineer/architect starting with ML
  • A Software team lead or manager overseeing ML teams
  • A Product Manager overseeing an ML product

Course Topics Include:

  • Recognize the key concepts, best practices, and applications of machine learning
  • Identify the most widely used machine learning algorithms and discuss their strengths and weaknesses.
  • Describe basic machine learning principles such as classification, regression, clustering, association learning, and dimensionality reduction.
  • Recall Python fundamentals, including basic syntax, variables, and types.
  • Build, train, and evaluate the performance of machine learning models using Python and its associated libraries.
  • Select the appropriate machine learning model for a given problem.
  • Perform exploratory data analysis on a dataset to detect anomalies and summarize its main characteristics

Recommended Prerequisites:

  • Moderate background in mathematics, especially statistics
  • Introductory level experience with Python
  • Intermediate level understanding of data analysis

Delivery Mode:

  • Learn on your own schedule with self-paced online training and labs
  • End date for access: August 2, 2022