000			03710nam a22002417a 4500
999			_c5016 _d5016
005			20230322120319.0
008			230322b ||||| |||| 00| 0 eng d
020			_a9789351199496
082			_a006.31 _bBRI
100			_aBrink, Henrik _911465
245			_aReal-world machine learning
260			_bDreamtech Publisher _aNew Delhi _c2020
300			_axxii, 242 p.
365			_aINR _b799.00
504			_aTable of content 1. What is machine learning? 1.1 Understanding how machines learn 1.2 Using data to make decisions 1.3 Following the ML workflow: from data to deployment 1.4 Boosting model performance with advanced techniques 1.5 Summary 1.6 Terms from this chapter 2. Real-world data 2.1 Getting started: data collection 2.2 Preprocessing the data for modeling 2.4 Summary 2.5 Terms from this chapter 3 Modeling and prediction 3.1 Basic machine-learning modeling 3.2 Classification: predicting into buckets 3.3 Regression: predicting numerical values 3.4 Summary 3.5 Terms from this chapter 4 Model evaluation and optimization 4.1 Model generalization: assessing predictive accuracy for new data 4.2 Evaluation of classification models 4.3 Evaluation of regression models 4.4 Model optimization through parameter tuning 4.5 Summary 4.6 Terms from this chapter 5 Basic feature engineering 5.1 Motivation: why is feature engineering useful? 5.2 Basic feature-engineering processes 5.3 Feature selection 5.4 Summary 5.5 Terms from this chapter 6 Example: NYC taxi data 6.1 Data: NYC taxi trip and fare information 6.2 Modeling 6.3 Summary 6.4 Terms from this chapter 7 Advanced feature engineering 7.1 Advanced text features 7.2 Image features 7.3 Time-series features 7.4 Summary 7.5 Terms from this chapter 8 Advanced NLP example: movie review sentiment 8.1 Exploring the data and use case 8.2 Extracting basic NLP features and building the initial model 8.3 Advanced algorithms and model deployment considerations 8.4 Summary 8.5 Terms from this chapter 9 Scaling machine-learning workflows 9.1 Before scaling up 9.2 Scaling ML modeling pipelines 9.3 Scaling predictions 9.4 Summary 9.5 Terms from this chapter 10 Example: digital display advertising 10.1 Display advertising 10.2 Digital advertising data 10.3 Feature engineering and modeling strategy 10.4 Size and shape of the data 10.5 Singular value decomposition 10.6 Resource estimation and optimization 10.7 Modeling 10.8 K-nearest neighbors 10.9 Random forests 10.10 Other real-world considerations 10.11 Summary 10.12 Terms from this chapter 10.13 Recap and conclusion
520			_aMachine learning systems help you find valuable insights and patters in data which you had never recognized in the traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior and make fact-based recommendations. It’s a hot and growing field and up-to speed ML developers are in demand. Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you’ll build skills in data acquisition and modelling, classification and regression.
650			_aMachine learning _92343
650			_aPython (Computer program language) _912421
650			_aData mining _9365
700			_aRichards, Joseph W. _912422
700			_aFetherolf, Mark _912423
942			_2ddc _cBK