Machine Learning Models
- No programming is required for this course. Required software and tools will be provided.
- Windows / MAC Laptop with at least 4GB RAM and an internet connection of at least 25 Mbps; Headset with microphone and webcam.
- Ability to communicate orally in English.
- Ability to take a college/university level course (undergraduate studies or equivalent work experience is ideal).
Machine Learning is one of the fundamental components of Artificial Intelligence, along with Deep Learning and Reinforced Learning. In this hands-on introductory course, students will learn about the fundamental principles of machine learning and the use cases of the most used machine learning algorithms nowadays. Python will be utilized to implement the different algorithms. A final project presentation might be required. In the first part of the class the instructor will deliver a live theorical and practical demonstration followed by questions from students; in the second part of the class, students will work on a specific assignment.
- Introduction to Machine Learning and Artificial Intelligence (2hrs)
- Supervised Learning Models: Regression (6 hrs.)
- Supervised Learning Models: Classification (6 hrs.)
- Supervised Learning Models: Hybrid and Ensembles (8 hrs.)
- Unsupervised Learning Models I: Clustering (6 hrs.)
- Unsupervised Learning Models II: Dimensionality Reduction (2 hrs.)
By the end of this course, students will be able to:
- Understand machine learning fundamental concepts and principles.
- Use Linear Regression as a benchmark model for regression.
- Use Logistic Regression as a benchmark model for classification.
- Classify text data using the Naive Bayes algorithm.
- Apply the Support Vector Machine algorithm to linear and non-linear data.
- Create KNN classification models for both regression and classification.
- Interpret regression and classification results using a decision tree.
- Create effective machine learning models by combining models into ensembles.
- Separate observations or data points in a data set using clustering.
- Reduce the number of features in a data set to those that are best for prediction.
Course Price: $575.00