000			04433nam a22002417a 4500
020			_a9789385889240
041			_aENG
082			_a006.31 _bTAT
100			_aTatsat, Hariom
245			_aMachine learning & data science blueprints for finance: _bfrom building trading strategies to robo-advisors using python/ _cHariom Tatsat, Sahil Puri and Brad Lookabaugh.
250			_aGrayscale edition.
260			_aUnited States of America: _bO'Reilly, _c2021.
300			_axv, 409 Pg. ; _bill. : _c21 cm.
500			_aOver the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
505			_aPart 1. The framework. Machine learning in finance: the landscape Developing a machine learning model in Python Artificial neural networks Part 2. Supervised learning. Supervised learning : models and concepts Supervised learning : regression (including time series models) Supervised learning : classification Part 3. Unsupervised learning. Unsupervised learning : dimensionality reduction Unsupervised learning : clustering Part 4. Reinforcement learning and natural language processing. Reinforcement learning Natural language processing
520			_aOver the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You'll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You'll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
650			_aNatural language processing
650			_aMachine Learning
650			_aPython
700			_aPuri, Sahil
700			_aLookabaugh, Brad
942			_2ddc _cBK
999			_c23940 _d23940