101 - Machine Learning Foundations
Everything you need to go from beginner to cleaning and automatically scraping data, writing algorithms in Python, applying machine learning techniques, and visualizing insights and findings.
We focus on the practical, not theoretical side of Data Science and Machine Learning. Our courses are tailored around teaching students how to extract insight from data using Machine Learning & AI without focusing too much on the Mathematics or mechanics behind it. Our courses are focused on practical learning with use cases and data sets from economics, social policy, ecommerce, marketing, and more. Reboot creates future economy ready employees by covering three core skill sets from the full data solutions pipeline. If you've ever been frustrated with the theoretical nature of other courses in ML and AI, then this is the course for you.
Who is it for?
Anyone looking to upskill themselves for the data economy of the future, and wants to learn cutting edge techniques to extract insights out of data
Familarity with programming concepts is helpful, though no previous knowledge of Python, data science, or machine learning is necessary. We will cover all the basics and build up from intuitive traditional methods to conceptually similar yet more powerful modern day machine learning techniques. All you need is your laptop and a hunger to learn!
Week 1 - Modern Landscape and Python Foundations
Machine learning landscape, introduction to Python, basic concepts in statistics, probability, and linear algebra.
Week 2 - Automated Data Scraping and Cleaning
Scraping custom data from the web, assessing data quality, preparing and cleaning data with normalization, reformatting, and dummy variables.
Week 3 - Python for "Quick Win" Insights
While designing our curriculum, hiring partners stressed that practical knowledge of 'quick and dirty' analysis was a vital skill that academic courses do not cover. Thus this week and Project 1 focuses on using popular Python libraries to quickly slice, plot, and visualize insights from data.
Week 4 - Algorithms & Machine Learning Intro
Linear and logistic regression for prediction and classification, organizing data for ML models, and building your first predictive algorithm from scratch in Python!
Week 5 - Machine Learning Core
Unsupervised learning with K-Means Clustering, non-linear classification and regression using Support Vector Machines, and Naive Bayes Classifiers.
Week 6 - Portfolio Project
Combine foundational knowledge with ML techniques to complete a project with instructor support, and add proof of your new knowledge to your portfolio!
Reboot is Hong Kong's only course provider trusted by industry partners
Head of Analytics and Data Science