Machine Learning with Python (10 ECTS)

Objective

This course dives into practical machine learning using an approachable and popular programming language, Python. It provides a unique opportunity for the student to get hands-on experience with popular Python libraries for machine learning. After passing this course, the student will be able to implement his/her machine learning models (supervised and unsupervised) from scratch and evaluate their performance. Furthermore, standard practices and tricks used by data scientists and machine learning experts are also described throughout the course to prepare the student for future job opportunities.

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

The student will pass this course after submitting the required quiz, assignments, and the final project

Content

1. Introduction:
Introduction to Machine Learning – Applications of Machine Learning – Why Python?

2. Python Basics:
Setting up Jupyter Notebook – Getting Started with Jupyter Notebook – Python Basics: Syntax and Variables – Python Basics: Operators – Python Basics: Data Structures – Python Basics: Decision Making – Python Basics: Loops

3. Python Libraries for Machine Learning
Numpy – Matplotlib – Pandas – Scikit-learn

4. Regression:
Linear Regression – Non-linear Regression

5. Classification:
Logistic Regression – Support Vector Machines – K-Nearest Neighbors – Decision Tree

6. Unsupervised Learning:
Principal Component Analysis – k-Means Clustering

7. Final Project

Location and time

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

Materials

Online.

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

Employer connections

N/A

Exam schedules

Online.

International connections

N/A

Completion alternatives

N/A

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.

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.

Assessment methods and criteria

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