Author Description

Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book • Understand which algorithms to use in a given context with the help of this exciting recipe-based guide • This practical tutorial tackles real-world computing problems through a rigorous and effective approach • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn • Use predictive modeling and apply it to real-world problems • Understand how to perform market segmentation using unsupervised learning • Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test • Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms • Increase predictive accuracy with deep learning and scalable data-handling techniques • Work with modern state-of-the-art large-scale machine learning techniques • Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-d