In this video, learn how to access the course exercise files and explore how they are set up.
- [Instructor] You'll have access to all the exercise files for this course, so you can follow along yourself and I highly recommend you do so. Particularly when it comes to learning through code there's no substitute for actually writing the code and debugging it yourself. So I highly encourage you to use the notebooks, write the code, tweak the code, and see how it changes things. The exercise files are organized into folders, one for each chapter. Each chapter folder is organized into folders for each individual lesson, and within each lesson you'll notice that there's a start and this is the notebook that you should start coding in and then an end, and this is the completed notebook just for reference. We're only using one dataset for this entire course and you'll find that dataset included as well. So you can run the notebooks just as they are. You'll also notice that each individual Jupyter notebook is titled with the chapter number, then underscore, and then the lesson number. So here you'll find the notebook for chapter one lesson five. So let's go ahead and jump over to the terminal and learn how to actually launch these Jupyter notebooks. So let's navigate to whatever folder is containing your notebooks. Here I have them stored in the exercise files on the desktop and then you just enter jupyter notebook, and then it'll launch in your browser and you can navigate the file structure and find whatever notebook you want to use. So now that you know what tools are required and how to use them let's get started.
- 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