To improve your skills in machine learning and artificial intelligence, it is important to solve real-world problems. What better problem to solve then helping to save people's lives? Machine learning is being used more and more in the field of healthcare. PyTorch and Monai can be used to discover tumors in livers. We just published a course on the freeCodeCamp.org

Real-World Machine Learning is a book for people who want to apply machine learning (ML) to their own real-world problems. It describes and explains the processes, algorithms, and tools that mainstream ML comprises. The focus is on the practical application of well-known algorithms, not building them from scratch. Each step in the process of building and using ML models is presented and illustrated through examples that range from simple to intermediate-level complexity.

Machine learning systems help you find valuable insights and patterns in data, which you?d never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It?s a hot and growing field, and up-to-speed ML developers are in demand.

Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems.

The primary goal of supervised machine learning is accurate prediction. We want our ML model to be as accurate as possible when predicting on new data (for which the target variable is unknown). Said in a different way, we want our models, which have been built from some training data, to generalize well to new data. That way, when we deploy the model in production, we can be assured that the predictions generated are of high quality. Therefore, when we evaluate the performance of a model, we want to determine how well that model will perform on new data.

It is, however, a thoughtful introduction to and overview of machine-learning methods, appropriately remembering about the context and life-cycle of an ML project, and keeping things hands-on with small Python examples, but managing not to fall into the catalogue mode. I have seen other books try this before. "Doing Data Science" by O'Neill and Schutt comes to mind first, long on enthusiasm but a little short on quality. Then there is Manning's own "Practical Data Science with R" by Zumel and Mount. Among the three, RWML looks like a clear winner. If I had to pick on something, I would register disappointment with the book's one extended exercise, based on the NYC taxi dataset.

Forget Big Data, Think Machine Learning - Jane Zavalishina, Yandex Data Factory - Duration: 22:45.