Full course description
Course created in collaboration with NMSU Computer Science.
This 8-week online course is designed to prepare you to apply supervised machine learning techniques for data analysis. Supervised learning is one major component of machine learning. The two most widely utilized supervised learning tasks, classification and regression, are discussed in the course. The classical classification methods include the perceptron model, logistic regression, support vector machines, decision trees, K-nearest neighbor, and ensemble approaches. The regression models include both linear and non-linear models. This course explains how to apply these machine learning models using Python’s scikit-learn library.
By the End of This Course, You Will be Able To:
- Utilize several classifiers and differentiate their advantages and disadvantages
- Explain and demonstrate regression analysis
- Apply ensemble learning approaches
Credit Received: Upon completion, learners will obtain a micro-credential for their digital backpack, demonstrating they have gained the skills necessary to begin work in this exciting field.
Course Facilitator: Erick Draayer
This certificate is beneficial to people working in the following roles:
- Data Scientists
- Head/VP/Manager/Director of Data Science
- Head of Machine Learning
"I learned a great deal on how to deal with classification problems and ... Also, the background info on how the algorithms were used in the class was really helpful in knowing what is really going on with each line of code. "
"I got familiar with different machine learning algorithms and their details. I am glad that this course used Python. I have always wanted to start learning this language but I thought it is so difficult and this class put me in a good situation to continue learning this famous programming language."
"I believe I have learned enough of machine learning application. I think I can implement or solve any ML problem on my own. I also learned... before taking this course I was very scared of programming as I am not from a computer science background. But after this course, I am confident about programming."
"I think the most important thing I learned was the application of various machine learning algorithms. Also, I learned how to analyze the performance of a model and the principles of different machine learning algorithms."