Machine Learning & AI

1.Introduction

• Machine Learning Introduction

• Environment Setup

• Models

• Supervised Learning: Regression, Regularization

• Model Preparation, Evaluation

• Classification

• Unsupervised Learning: Clustering, Dimensionality Reduction

• TensorFlow

• PyTorch

3. Final project

• You can commit at least 10 hours a week (classes + self-study), 80% attendance of the course is required for graduation.

• You are able to understand and speak English. You have experience in self-study

• You have a good understanding of Python

• You have passed the equivalent of Python Intermediate.

• You have an interest in Data Science and/or Machine Learning

Prepare yourself for the course with the following self-learning contents:

• Discover data analysis on MS Learn

• Introduction to Machine Learning on MS Learn

• IBM SkillsBuild: Machine Learning with Python (Do Modules 0 and 1)

Registered on dataquest.io and finished the following modules:

• Python Intermediate: Cleaning and Preparing Data in Python

• Pandas and NumPy Fundamentals (first 3 modules)

• Elements of the Command Line (first module only)

Note: Graduates from our Python Foundation course will be preferred!

You’ll be able to:

• Be familiar with Data Science methods and tools.

• Use Machine Learning Frameworks

You have a Data Science mindset

This course is held completely online via Zoom. It is important that you can attend both sessions per week.

In addition, homework, group projects and your personal study takes place online.

• Career & Soft Skills Workshops, Company Visits

• ReDI Mentorship Program (mentors in the IT industry)

• ReDI Talent Pool (job listing platform)

• SkillBuild self-paced eLearning (by IBM & ReDI)

• You should expect to spend a minimum of 8 hours for career workshops or trainings during the semester.