Paige Jacobs

Machine Learning with Python Paige Jacobs

Learning is a crucial factor in intelligence. The realization of intelligent systems by computers, which are not programmed but trained, is the goal of Artificial Intelligence. Machine learning deals with the necessary methods and algorithms to provide artificial intelligence. These formulate different learning objectives, address diverse application areas, and make different demands on existing data. Anyone who wants to intelligently use more substantial amounts of data to generate added value from them needs an overview of machine learning. On the other hand, a deeper algorithmic understanding is required to estimate effort and to increase success rates through adjustments. The aim of the book is, therefore, to make learners fit for machine learning (theoretical and practical). We will work with Python and related libraries offering open-source and state-of-the-art implementations. Also, we look at aspects of machine learning in the cloud with concrete examples. The "Advanced Machine Learning with Python" teaches the formalization of learning problems, methods for dimensionality reduction and input engineering as well as ensemble methods. Participants will be familiar with the Python Machine Learning tools following the training. Machine learning, as part of artificial intelligence, is about using the right features to construct the right models for solving a specific task. Models are nothing more than the output of algorithms applied to the data. We learn which algorithms exist for which tasks and how we can use them with Scikit-learn in Python. We'll go through advanced aspects, such as scalability of solutions and the combination of models, as well as discussing deep learning, currently the hottest topic in machine learning. Machine learning doesn't work for any particular industry; instead, it works in virtually all of them in some way. We have aimed the further education module at all those who are already analyzing data or would like to do more in the future and would like to acquire more competences. If you want to understand how Python can help answer critical questions about your data, you've come to the right place. Whether you are a beginner or want to deepen your knowledge in the field of data science, this book is an indispensable source of information and well worth the time it will take to read. Now is your chance to advance your knowledge of machine learning with the use of Python. What are you waiting for? Get started now!