Jesus Salcedo - Machine Learning for Data Mining

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Jesus Salcedo has a PhD in psychometrics from Fordham University. He is an independent statistical consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.

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30% of data mining vacancies also involve machine learning. And those that do are 30% better paid than the rest. If youre involved in data mining, you need to get on top of machine learning, before it gets on top of you.

Hands-On Machine Learning for Data Mining gives you everything you need to bring the power of machine learning into your data mining work. This book will enable you to pair the best algorithms with the right tools and processes. You will see how systems can learn from data, identify patterns, and make predictions on data, all with minimal human intervention.

If you are a data mining professional who wishes to get a ticket to a 30% higher salary by adding machine learning to your skill set, then this is the ideal course for you. No prior knowledge in machine learning is assumed.

, Introducing Machine Learning Predictive Models , introduces you to the theory behind predictive models, looking at how they work and providing an insight into types of predictive modeling, such as the neural network model, which is explained in brief in this chapter.

, Getting Started with Machine Learning , introduces you to the implementation of a neural network model, and gives an insight into the implementation of Support Vector Machines (SVMs) as well.

, Understanding Models , explains different types of models and the situations in which each of them should ideally be used.

, Improving Individual Models , shows you different ways in which we can improve our models. This chapter will show you four methods to improve the accuracy of your model.

, Advanced Ways of Improving Models , focuses on combining different models in different ways to get increasingly better results. In this chapter, we will see how a certain part of a dataset, which doesn't contribute much to the results of a neural network model, performs very well on the CHAID and C5.0 decision tree models. We will also see how to model the errors to prepare our models.

• Some knowledge on what data mining is, and the basic concepts of machine learning, will act as starting points for this book.
• Familiarity with any machine learning modeler, specifically the SPSS Modeler provided by IBM, will be a plus, but isn't necessary.

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