It's almost back to school, and we've got you covered with our new free online data science course.
We've been working for years on making machine learning — or as the kids are calling it these days AI — accessible to more people, including our clients, but also free users through our academic and non profit partnerships, as well as the free version of our data science software.
We've been teaching data science for a while now, to new marketing recruits (yours truly included), to partners, clients, at meetups, conferences, workshops, trainings, and through courses and talks at some of the top universities in the world.
So we thought we'd take some of that stuff and make it accessible to everyone.
Why? Because we believe that in today's business everyone should understand how data works and how algorithms can be harnessed to create more value. Heck, in the world we live in it's more and more vital to know what goes into a Facebook article recommendation or a self-driving car.
A Better Way to Learn Data Science
We believe that the key to getting started with data science is to work on concrete use cases and build your own projects fast. Theory is important of course, and we'll go over the basics so you know what you're talking about, but the best way to know machine learning is to do it.
That's why this course will be focused on teaching you the basics, but more importantly, on giving you practical skills to understand and solve actual business use cases. We're offered four free online course for you to get started with the basics of machine learning and data science - access the recording — as well as the slides — for all of the courses.
Learning the Basics, Concepts, and Your First ML Model
- Starting with definitions: what are we talking about
- Changing mindsets: going from small to big data
- Practical: Training your first ML model
The Data Science Workflow, Building a Predictive Model Flow
- The 6 steps of building a predictive model, from business project to deployment
- Prediction vs Clustering — what's your use case?
- Practical: From data to model in 15 minutes
Getting Dirty; Data Preparation and Feature Creation
- Categorical, numerical, text: how does the model read your data?
- Good data = good model, or why data cleaning is 80% of your data project
- Practical: Data cleaning and feature creation
Understanding Your Model — and Communicating About It
- After the training: understanding how your model worked
- Communicating results and thinking about production
- Practical: Model performance and building graphs