About the Course
This course dives into the basic of machine learning using Python, an approachable and well-known programming language. You will learn about supervised and unsupervised learning, analyze the relation between statistical modelling and machine learning through comparisons.
You will also look into real-life examples to look at the various ways that machine learning affects the society. You will also explore many algorithms and models –
- Popular algorithms: Classification, Regression, Clustering, Dimensional Reduction
- Popular models: Train/Test Split, Root Mean Squared Error, Random Forests
Most importantly, this course will transform your theoretical knowledge into practical skills through experience at many hands-on labs.
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
- Linear Regression
- Non-linear Regression
- Model evaluation methods
- K-Nearest Neighbour
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
- Content-based recommender systems
- Collaborative Filtering
Prerequisites for this Course
Recommended Existing Skills
- JupyterLab. You can enrol for our free course for JupyterLab here.
- Data Analysis with Python. You can take the course here.
Saeed Aghabozorgi, PhD.
Saeed Aghabozorgi, PhD. He is a Sr. Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge. He is a data mining field researcher and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.
Agatha Colangelo also contributed.