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003			IIITD
005			20240602020003.0
008			231213b xxu||||| |||| 00| 0 eng d
010			_a 2023276388
015			_aGBC290257 _2bnb
016			_a020621576 _2Uk
020			_a9781098102937
035			_a(OCoLC)on1328015167
040			_aUKMGB _beng _erda _cUKMGB _dFIE _dOCLCF _dUAP _dNVC _dJRZ _dOCL _dVNVGU _dIIITD
042			_alccopycat
050	0	0	_aQA76.9.D343 _bN54 2022
082			_a006.310 _223 _bNIE-E
100			_aNield, Thomas
245			_aEssential math for data science : _btake control of your data with fundamental linear algebra, probability, and statistics _cby Thomas Nield
260			_aMumbai : _bShroff Publishers, _c©2022
300			_axiv, 332 p. : _c24 cm. _bill. ;
500			_aThis book includes index.
505			_t1. Basic math and calculus review _t2. Probability _t3. Descriptive and inferential statistics _t4. Linear algebra _t5. Linear regression _t6. Logistic regression and classification _t7. Neural networks _t8. Career advice and the path forward
520			_aTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks --
650			_aData mining _xMathematics.
650			_aMachine learning _xMathematics.
650			_aMathematical statistics.
650			_aProbabilities.
650			_aComputer science _xMathematics.
650			_aComputer science _xMathematics. _2fast
650			_aData mining _xMathematics. _2fast
650			_aMathematical statistics. _2fast
650			_aProbabilities. _2fast
655		7	_aHandbooks and manuals. _2fast
655		7	_aHandbooks and manuals. _2lcgft
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _03
999			_c171992 _d171992