The world’s leading publication for data science, AI, and ML professionals.

Don’t just take notes – turn them into articles and share them with others

An interview with Alexey Grigorev, author of the book- Machine Learning Bookcamp.

A series of interviews, highlighting the incredible work of writers in the space of data science and their path of writing.

Photo courtesy of Alexey Grigorev
Photo courtesy of Alexey Grigorev

"While it is wise to learn from experience, it is wiser to learn from the experiences of others."-Rick Warren

The above quote by Rick Warren emphasizes the importance of learning from others. In the context of data science, that’s even more meaningful. Many of us (including me) have benefitted tremendously from the work of others. Be it open-source software, content in the form of blog posts and papers, and meetup events and conferences, we keep learning from the work and experience of others in the field.

In an endeavor to bring such notable work to the forefront, I started an interview series last year. During the first season, I presented stories from established data scientists and Kaggle Grandmasters, who shared their journey, inspirations, and accomplishments. For the second season, I’m interviewing book Authors. As a writer myself, I have tremendous respect for people who write books. A single well-written article takes a lot of time, energy, and patience, and to replicate the same for a book is no mean feat. As such, this edition of the interviews will bring to light the story of some of the well-known authors in the data science field.


Meet the Author: Alexey Grigorev

If you are into Data Science and follow the communities in this space, I’m sure you have heard about the DataTalks.Club – a community for data enthusiasts. It is currently among the most active communities when it comes to all things data science. As the name suggests, the club organizes talks about data, machine learning, and engineering, including weekly events, conferences, and office hours. The cherry on top – the club is also hosting a free machine learning course currently!

The man behind this mammoth initiative is Alexey Grigorev, a principal data scientist at OLX. He lives in Berlin along with his wife and son. I have witnessed the steady rise of this community and was fortunate enough to be a speaker at one of the events. Alexey has also authored a couple of books, and this interview is an effort to get to know more about his recent book titled Machine Learning Bookcamp.


Q: How did the idea of this book originate?

Alexey: It’s not my first book. I wrote a couple of others before Machine Learning Bookcamp. One of them, which I co-authored (TensorFlow Deep Learning Projects), was a project-based book – each chapter was an end-to-end project. I liked this format, and when Manning reached out and suggested writing a book, we decided to follow a similar format. We made it project-based, and that’s how it originated. After that, we took a few months to think it through, create an outline, get it reviewed, and iterate on it.


Q: Could you summarize the main points covered in the book for the readers?

Alexey: The book typically covers three main points:

  • Machine learning is not magic. You need data – a feature matrix X and a target variable y. Then you put it into a Machine learning algorithm – a bunch of math coded in a programming language – and get a model. After that, you can use the model to make predictions.
  • You don’t need to know a lot of math to get started with machine learning. There are libraries like Scikit-Learn that you can use to build machine learning services. First, focus on learning how to use these libraries.
  • Deploying models is one of the most critical parts of the machine learning process. If you can’t deploy your model, others can’t benefit from it – and even the most accurate models become useless.

Q: Who is the target audience for the book?

Alexey: The reader I had in mind when writing the book was me seven years ago. I was a software engineer interested in machine learning. I took a lot of courses, more than twenty in total. But all this theoretical knowledge turned out to be useless when I found Kaggle and tried competing for the first time.

It was on Kaggle where I learned machine learning. It worked for me because I had to focus on the problem and then learn by solving it. I think this is the best approach for getting into machine learning for software engineers. And this is precisely the approach I follow in Machine Learning Bookcamp.

Image by Author
Image by Author

Even though I was writing it for software engineers, many others also found this book useful. Data analysts liked the Model evaluation chapter, and data scientists enjoyed the Model deployment chapters.


Q: How can a reader make the most out of this book?

Alexey: I would suggest the following to make the most out of this book:

  • Don’t just read the book – open your Jupyter Notebooks and follow along.
  • Don’t skip the "explore more" sections and do the suggested projects to solidify your learning.
  • Don’t skip the deployment chapters – they are essential. A model is only useful when others can use it.

Q: What advice would you give a new writer, someone just starting out?

Alexey: Learn in public. Don’t just take notes – turn them into articles and share them with others. As a bonus, if you do that, you’ll discover that you learn much better. If you want to write a book, you can start by volunteering as a book reviewer. I was reviewing books for some time before writing my first one. Also, I interviewed Eugene Yan, who’s quite good at writing. Here’s a couple of links from him:


Q: How long did it take for you to finish writing the book? More importantly, how did you manage to write a book along with your job?

Alexey: It took almost three years – from the moment I got contacted by Manning and by the time I received a printed copy. For the first six months, we were figuring out the outline and getting a lot of feedback. Then I wrote the first two chapters. Later, we split these two chapters into five and released them as a MEAP (early access program from Manning).

Over time, I had to drop a lot of chapters I initially planned to include and focus on explaining the basics. I also re-iterated the content of the existing chapters.

Image by Author
Image by Author

Manning helped a lot with making sure the quality was excellent. They organized three feedback rounds from the reviewers – the first one before launching the MEAP, the second one after 60% was finished, and the last one – when the entire book was done.

Writing a book and working at the same time was very challenging. That’s why it took so much time. I don’t have any secrets. I was slacking a lot and was on the verge of giving up a couple of times. The main reason I didn’t was because of MEAP and due to the sense of accountability. Many people had already bought the book, so I couldn’t just stop working on it.


Q: Do you have a favorite book and author (in technical or non-technical space)?

Alexey: I used to love Tolkien, but these days I don’t read non-technical books.

In the technical space, my favorite one is "Designing Data-Intensive Applications" by Martin Kleppmann. I can’t imagine how much effort Martin put into this book. It’s very well structured, and I recommend looking at it even if you’re not interested in data engineering. You’ll learn a lot about technical writing just by reading this book.


👉 Read other interviews in this series:

You do not become better by employing fancy techniques but by working on the fundamentals

👉 Looking forward to connecting with Alexey? Follow him on Twitter and LinkedIn.


Related Articles