Machine Learning with Python (10 ECTS)

Location and time

Course is online in TechClass environment and it can be done in own pace.

Materials

Online.

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

Evaluation methods and criteria

Exercises 30%
Quizzes 20%
Project 40%
Essay 10%

Student workload

Lectures = 40h
Assignments = 25h
Self-study = 40h
Quiz = 5h
Project = 20h
Essay = 5h
Total = 135 hours

Content scheduling

Up to Student her-/himself.

Teaching methods

his course is 100% virtual thanks to the comprehensive interactive material and content prepared for this course.

Course includes:
- Tutorial Videos
- Exercises
- Quiz
- Project
- Self-study

Evaluation scale

Hyväksytty/Hylätty

Assessment criteria, satisfactory (1)

- The student knows the basic concepts of machine learning.
- The student knows the general framework of machine learning algorithms and their primary types.
- The student is familiar with real-life applications of machine learning.
- The student is familiar with the history of Python and why it is essential for machine learning.
- The student knows how to set up and get started with the Jupyter Notebook for Python.
- The student is familiar with writing codes in Python programming language.
- The student is familiar with important Python libraries for machine learning.

Assessment criteria, good (3)

- The student knows how to work with Numpy arrays and how to use different Numpy functions to operate on them.
- The student knows how to use Matplotlib to produce basic plots of data and results.
- The student knows how to use the Pandas library to work with tabular data in order to manipulate them.
- The student is familiar with the Scikit-learn library and its importance in building machine learning models.
- The student knows how to train and evaluate linear regression models using Scikit-learn.
- The student knows how to implement simple non-linear regression models using Scikit-learn.
- The student is familiar with data preprocessing and how to perform it using Pandas and Scikit-learn libraries.
Englanniksi
- The student knows how to work with Numpy arrays and how to use different Numpy functions to operate on them.
- The student knows how to use Matplotlib to produce basic plots of data and results.
- The student knows how to use the Pandas library to work with tabular data in order to manipulate them.
- The student is familiar with the Scikit-learn library and its importance in building machine learning models.
- The student knows how to train and evaluate linear regression models using Scikit-learn.
- The student knows how to implement simple non-linear regression models using Scikit-learn.
- The student is familiar with data preprocessing and how to perform it using Pandas and Scikit-learn libraries.

Assessment criteria, excellent (5)

- The student knows how to train and evaluate logistic regression, support vector machines, K-nearest neighbors, and decision tree classifiers using Scikit-learn.
- The student knows how to tune some parameters of learning algorithms to achieve better performances.
- The student knows how to implement the PCA method using Scikit-learn for dimensionality reduction.
- The student knows how to implement the k-means method using Scikit-learn to perform clustering on unlabeled data.
- The student knows how to visualize data and model performance efficiently.
- The student knows how to use different metrics to facilitate model evaluation and selection.