Practical Machine Learning is aimed at being a guidebook for both . Did you know that Packt offers eBook versions of every book published, with PDF. Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage. Software Developer at Government Digital Service. • Python/Django developer. • passionate about micro controllers, IoT, Arduino, Golang and machine learning.
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Learn how to build Machine Learning applications to solve real-world data analysis challenges with this Machine Learning book – packed with practical tutorials. The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for. Germany, the. Practical Machine Learning with Python. A Problem-Solver's The Machine Learning Pipeline. Front Matter. Pages PDF · Processing, Wrangling, and.
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Become a contributor. Go to Subscription. You don't have anything in your cart right now. This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles.
Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data. This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout.
Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application.
With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data.
The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory—and mystery—out of even the most advanced machine learning methodologies. She has close to 14 years of rich hands-on experience in the IT services space.
She currently runs the Architecture Center of Excellence from India and plays a key role in the big data and data science initiatives.
Prior to joining Broadridge she held key positions at leading global organizations and specializes in Java, distributed architecture, big data technologies, advanced analytics, Machine learning, semantic technologies, and data integration tools. Sunila represents Broadridge in global technology leadership and innovation forums, the most recent being at IEEE for her work on semantic technologies and its role in business data lakes.
Sunila's signature strength is her ability to stay connected with ever changing global technology landscape where new technologies mushroom rapidly, connect the dots and architect practical solutions for business delivery. She's a noted Indian classical dancer at both national and international levels, a painting artist, in addition to being a mother, and a wife.
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Book Description This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. The Python Machine Learning Ecosystem. Processing, Wrangling, and Visualizing Data. Feature Engineering and Selection. Building, Tuning, and Deploying Models. Analyzing Bike Sharing Trends.
Analyzing Movie Reviews Sentiment. Customer Segmentation and Effective Cross Selling. Analyzing Wine Types and Quality. Analyzing Music Trends and Recommendations. Forecasting Stock and Commodity Prices. Deep Learning for Computer Vision.
Back Matter Pages About this book Introduction. Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering.
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