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The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

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The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur - okladka książki

The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur - okladka książki

Autorzy:
Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Wydawnictwo:
Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
532
Dostępne formaty:
     PDF
     ePub
     Mobi

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Prezent last minute w ebookpoint.pl
Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.

Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models.

Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.

By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.

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O autorach książki

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.
Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer.

Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

Benjamin Johnston zajmuje się zaawansowaną analizą danych w branży medycznej. Interesuje się uczeniem maszynowym, przetwarzaniem obrazów i sieciami neuronowymi.

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur - pozostałe książki

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