Chapter 12 Concluding Remarks

I hope the previous chapters gave you a basic but sound foundation about how to use machine learning models for research or applications in your industry. Especially, I hope that the book sparked your interest in exploring machine learning further.

This leads to the question, where to go next? What is a good strategy to further extend your machine learning knowledge? There are many answers to this question. We will focus on six strategies:

1. Extend your knowledge about the mathematical foundations of machine learning models. One of the standard machine learning books that gives you a sound mathematical introduction is Introduction to Statistical Learning by James et al. (2023).

2. Extend your machine learning toolkit. We used the tidymodels package as a toolkit in the previous chapters of this book. The tidymodels package offers two distinctive strengths: i) It standardizes the data and modeling workflow, and ii) it provides many strong machine learning tools. We focused on the standardization aspect of machine learning that tidymodels provides. If you would like to take advantage of the analytical strength of tidymodels, then Tidy Modeling with R by Kuhn and Silge (2022) is the right place to start your journey.

3. Apply machine learning directly to your field of research or a use case in your industry, such as finance or marketing. For the latter fields, we recommend Tidy Finance with R by Voigt, Weiss, and Scheuch (2023) and Hands-On Data Science for Marketing by Hwang (2019), respectively.

4. At some point in time, you might need to review topics from this book. The best way to find a topic you are interested in is to go to the Learning Outcomes section of the related chapter. There, you can review the learning outcomes and then go to the section(s) you would like to read again. You can also use the AI assistant from the companion website: https://ai.lange-analytics.com/gen-ai/

5. Take online classes. Although this can be expensive in some cases, free or less costly high-quality options are also available. For example, EdX provides excellent online courses authored by faculty from Harvard, UC San Diego, and other top universities. One course we especially recommend is Machine Learning Fundamentals. It introduces machine learning in a more formal way than this book, but the course is still intuitive. EdX courses are free as long as you do not require a certificate.

   Another good source for online courses is DataCamp. The DataCamp learning platform provides a wide range of data science and AI online courses with readings, videos, and exercises. Some courses are free, and the first chapters of all other courses are free to read. This way you can find out if DataCamp is for you and if you want to subscribe for $25/month.

6. Extend your machine learning skills with a more random strategy. As you know from machine learning (see Bootstrapping in Chapter 10), randomization can be a solid tool to reach a goal.

   For a more random learning strategy, signing up to Medium might be a good option. Depending on your preferences and reading history, you get a daily email with recommendations for articles to read. If you think that you do not have the time, you could read in the evening in bed, and this way, carve the needed time out of your busy schedule.

   Read the articles without pressure but with enjoyment. If you are tired after a few pages, stop reading and sleep. Otherwise, read the complete article and sleep a little later. In any case, you win either by falling asleep smoothly or improving your skills. Signing up to Medium is free, and many articles are free to read. In addition, you can read up to three “members only” articles for free. If you would like to read more, you can consider a subscription for $50/year.

Whatever strategy you use to extend your knowledge in machine learning, I wish you an enjoyable journey.

Happy Analytics!

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