Preface
This book, titled Machine Learning Solutions, gives you a broad idea about the topic. As a reader, you will get the chance to learn how to develop cutting-edge data science applications using various Machine Learning (ML) techniques. This book is practical guide that can help you to build and optimize your data science applications.
We learn things by practically doing them. Practical implementations of various Machine Learning techniques, tips and tricks, optimization techniques, and so on will enhance your understanding in the ML and data science application development domains.
Now let me answer one of the most common questions I have heard from my friends and colleagues so frequently about ML and the data science application development front. This question is what really inspired me to write this book. For me, it's really important that all my readers get an idea of why am I writing this book. Let's find out that question…!
The question is, "How can I achieve the best possible accuracy for a machine learning application?" The answer includes lots of things that people should take care of:
- Understand the goal of the application really well. Why does your organization want to build this application?
- List down the expected output of the application and how this output helps the organization. This will clarify to you the technical aspect and business aspect of the application.
- What kind of dataset do you have? Is there anything more you need in order to generate the required output?
- Explore the dataset really well. Try to get an insight from the dataset.
- Check whether the dataset is having labels or not. If it is a labeled dataset, then you can apply supervised algorithms; if it is not labeled, then apply unsupervised algorithms. Your problem statement is a regression problem or classification problem.
- Build the very simple base line approach using simple ML techniques. Measure the accuracy.
- Now you may think, "I haven't chosen the right algorithm and that is the reason the accuracy of the base line approach is not good." It's ok!
- Try to list down all the possible problems that you can think your base-line approach has. Be honest about the problems.
- Now solve the problems one by one and measure the accuracy. If the accuracy is improving, then move forward in that direction; otherwise try out other solutions that eventually solve the shortcomings of the base line approach and improve the accuracy.
- You can repeat the process number of times. After every iteration, you will get a new and definite direction, which will lead you to the best possible solution as well as accuracy.
I have covered all the specified aspects in this book. Here, the major goal is how readers will get a state-of-the-art result for their own data science problem using ML algorithms, and in order to achieve that, we will use only the bare necessary theory and many hands-on examples of different domains.
We will cover the analytics domain, NLP domain, and computer vision domain. These examples are all industry problems and readers will learn how to get the best result. After reading this book, readers will apply their new skills to any sort of industry problem to achieve best possible for their machine learning applications.