000			02150nam a22002057a 4500
005			20240213192900.0
008			240213b |||||||| |||| 00| 0 eng d
020			_a9789389328622
082			_a330.0285631 _bSIN
100			_aSingla, Saurav _914307
245			_aMachine learning for finance: _bbeginner's guide to explore machine learning in banking and finance
260			_bBPB Publications _aNew Delhi _c2023
300			_axxi, 217 p.
365			_aINR _b799.00
520			_aThe fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation. The book demonstrates how to solve some of the most common issues in the financial industry. The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Naïve Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. (https://in.bpbonline.com/products/machine-learning-for-finance?_pos=1&_sid=1005cd63f&_ss=r)
650			_aMachine learning _915068
650			_aFinance--Data processing
650			_aFinance--Mathematical models _915769
942			_cBK _2ddc
999			_c6050 _d6050