Krish Naik - Hands-On Python for Finance

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First published: March 2019

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ISBN 978-1-78934-637-4

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Krish Naik works as a lead data scientist, pioneering in machine learning, deep learning, and computer vision, and is an artificial intelligence practitioner, an educator, and a mentor, with over 7 years' experience in the industry. He also runs a YouTube channel where he explains various topics on machine learning, deep learning, and AI with many real-world problem scenarios. He has implemented various complex projects involving complex financial data with predictive modeling, machine learning, text mining, and sentiment analysis in the healthcare, retail, and e-commerce domains. He has delivered over 30 tech talks on data science, machine learning, and AI at various meet-ups, technical institutions, and community-arranged forums.

Arunkumar N T has attained an MSc (physics) and an MBA (finance), pursuing CMA and CS. He has over 20 years' experience of corporate life and 2 years' experience teaching MBA students. He is an entrepreneur and has previously worked for Airtel, Citi Finance, ICICI Bank, and many other companies.

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The main purpose of this book is to provide a detailed and intensive introduction to the use of Python in finance. It explores the key characteristics of this powerful and modern programming language to solve problems in finance and risk management.

This book will explain how to code in Python and how to apply these skills in the world of finance. It is both a programming and a finance book. It will provide hands-on experience of various data analysis techniques that are relevant for financial analysis in Python, and of machine learning using sklearn and various stats libraries.

This book explains techniques, from basic to advanced, for applying statistical methods that will be useful for data preprocessing and to predict some real-world scenarios, such as stock prediction and sales prediction. It covers model creation using deep learning techniques, which involve neural network and recurrent neural network concepts with preprocessed financial data including stock prices and sales data, and you will be able to create a predictive model.

This book will also deal with lots of projects related to financial data, and we will see how different machine learning and deep learning algorithms are applied, and how various information and insights are gained from the data in order to make predictions.

At the time of writing in 2019, this book will have the most up-to-date code on Python, along with the latest libraries and techniques used for preprocessing financial data. This book also covers creating models using the best machine learning and deep learning techniques with open source software libraries such as TensorFlow and Keras.