Alberto Boschetti – författare

Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsKey Features[*]Design, engineer and deploy scalable machine learning solutions with the power of Python[*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework[*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleBook DescriptionLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.What you will learn[*]Apply the most scalable machine learning algorithms[*]Work with modern state-of-the-art large-scale machine learning techniques[*]Increase predictive accuracy with deep learning and scalable data-handling techniques[*]Improve your work by combining the MapReduce framework with Spark[*]Build powerful ensembles at scale[*] Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machineWho this book is forThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.

Learn to solve challenging data science problems by building powerful machine learning models using PythonAbout 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 scaleWho This Book Is ForThis 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 techniquesIn DetailMachine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:* Python Machine Learning Cookbook by Prateek Joshi* Advanced Machine Learning with Python by John Hearty* Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca MassaronStyle and approachThis course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!

Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.