Generate impactful insights with the power of machine learning. Get the foundational skills needed to efficiently solve nearly any kind of machine learning problem.
- [Instructor] Have you ever wondered how when you upload a picture to Facebook it can find peoples faces and even suggest who it may be? Or how Google Maps can predict the fastest route based on traffic? If you answered yes to either of those questions, then this is the right course for you. Hi, I'm Derek Jedamski, a data scientist with a passion for machine learning. I would like to welcome you to Applied Machine Learning, the Foundations. In this course, I'm going to distill many of the complexities of machine learning into a handful of key foundational concepts that you can build upon. The amount of data generated by machines and humans is mind boggling and it's growing faster than it ever has. According to IBM, 90% of the data ever created was created in the last two years. Imagine, all the power that exists in that data. If we could just harness it and use it in the right way. The challenge is that a lot of this data is completely unstructured and really messy. Or maybe there's simply too much data to even know how to extract value out of it. This course will give you the toolkit needed to wrangle unwieldy data and translate it into a form that could be used to generate some incredibly powerful insights by way of machine learning. More specifically, this course will cover some of the foundations of machine learning like exploratory data analysis, cleaning your data, fitting robust models, tuning hyperparameters, and finally, evaluating a model to ensure that it generalizes to unseen examples. Once you finish this course, you'll have all the tools to harness unstructured data of all types to go from that messy data to concise and accurate predictions from a machine learning model. This will allow you to deliver powerful solutions to complex business problems. Let's get started.
- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model