In this video, learn what you should already know before starting this course on machine learning.
- [Instructor] Before we get started I want to go over some basic assumptions about the background knowledge that will help you get the most out of this course as well as some other concepts that would be helpful but not necessary. First, it will be helpful if you have some entry level knowledge with Python. There won't be anything too complex but I won't be covering the most basic concepts so it will be helpful if you broadly understand how it works and some of the basic syntax. Beyond that it'll be helpful if you've had some experience with the NumPy, pandas, and scikit-learn libraries as we'll be relying on each of these fairly heavily throughout this course. Lastly, we're focusing on machine learning algorithms here. So having some familiarity with foundational machine learning concepts would be helpful. It's okay if you don't know them inside and out we'll be reviewing those machine learning foundations in the first chapter. Some experience handling data or doing some basic data analysis or data manipulation would also be helpful but it's not required. Lastly, if you have some experience fitting basic models that might be helpful just to enable you to really dive in on these new algorithms but if you don't have experience building machine learning models don't worry we will still cover the foundations you need to know to get the most out of this course.
- Models vs. algorithms
- Cleaning continuous and categorical variables
- Tuning hyperparameters
- Pros and cons of logistic regression
- Fitting a support vector machines model
- When to consider using a multilayer perceptron model
- Using the random forest algorithm
- Fitting a basic boosting model