Fundamentals of Machine Learning in Data Science
Curriculum guideline
Lecture/Seminar: 4 Hours per week
Lecture, seminars, demonstrations, and hands-on exercises/projects
- Programming language review for data analytics
- Basic syntax, variables, control flow, loops, install and import libraries for data processing such as SciPy, NumPy, Pandas, Sci-Kit Learn, TensorFlow or other similar libraries and packages
- Data And Features: using libraries such as NumPy and Pandas
- Represent data using lists, arrays for structured data
- Work with data frames using packages such as Pandas to represent diverse data
- Use Control Flow for filtering data and performing filtered computations
- Manipulate Data using functions and packages to process the data and perform computations
- Understand, determine and represent Features
- Perform Data Wrangling
- Exploring Data: - using libraries such as Matplotlib
- Visualize Data by creating plots using tools such as Matplotlib
- Perform high dimensionality visualizations
- Transforming Data: using libraries such as Sci-Kit learn
- Create Data Transformers and apply dimensionality reducing techniques as PCA
- Data Modeling: using libraries such as Keras, TensorFlow and scikit-learn
- Use machine learning techniques such as clustering, supervised learning, K-nearest neighbours, Regression to model the data
- Evaluating Data: Evaluate modeled data using evaluation techniques
- Create and apply confusion matrices
- Perform cross-validation using scoring metrics
- Implement and apply power tuning and pipelining to evaluate the data
At the end of this course, the successful student will be able to:
- Use appropriate tools and libraries needed for data science.
- Explain the concepts of data processing and feature engineering.
- Conduct data wrangling for further machine learning processing.
- Apply statistisc and visulization techniques for data exploration.
- Employ suitable structures to transform data for modeling.
- Utilize various machine learning models and algorithms for different applications.
- Compare different models using appropriate evaluation metrics and strategies.
The course evaluation is consistent with the Douglas College Evaluation Policy.
| Labs | 0 - 10% |
| Assignments | 0 - 10% |
| Projects | 15% - 30% |
| Midterm Exam* | 30% - 40% |
| Final Exam* | 30% - 40% |
| Total | 100% |
* Practical hands-on computer exam
In order to pass the course, students must, in addition to receiving an overall course grade of 50%, also achieve a grade of at least 50% on the combined weighted examination components (including quizzes, tests, exams).
Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.
Custom courseware, class notes provided by the instructor, and online resources or other textbooks as approved by the department
CSIS 1175 (minimum grade C)