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7 books on AI Predictive Analytics [PDF]
October 24, 2024 | 21 |
These books are covering topics such as machine learning, data preprocessing, feature selection and model evaluation that are used in building accurate predictive models for data-driven decision-making in various industries.
1. Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
2023 by Felix Weber
Imagine, if you will, a universe where businesses, drowning in oceans of data, cry out for a savior—not in the form of a caped hero, but a glittering beacon of Artificial Intelligence. Artificial Intelligence for Business Analytics isn’t just a book; it’s a galactic hitchhiker’s guide to surviving the bewildering cosmos of algorithms, platforms and applications. With a blend of machine learning wizardry, actionable business scenarios and a Business Analytics Model for AI that’s as reassuring as a towel in a crisis, Felix Weber takes you on a whirlwind tour of how to make sense of your data before it swallows you whole. Think predictive insights, automated decisions and AI magic—all served up with the stark realization that clinging to traditional business intelligence is about as useful as a broken-down hyperspace drive. Embrace it, or be left behind with the Vogons.
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2. The Power of AI Prediction
2023 by HUATENG OU
In a universe teetering on the edge of chaos (or at least a slightly disorganized filing cabinet), The Power of AI Prediction cheerfully unpacks the mind-boggling brilliance of artificial intelligence, which, it turns out, is less about murderous robots and more about politely foreseeing what you might want for breakfast. HUATENG OU serves up a delightful exploration of how AI isn’t just predicting trends—it’s practically rolling out the red carpet for tomorrow. With real-world examples that are almost too clever to be true and an enthusiasm for transforming society that’s so infectious you might want to wear gloves, this book invites business moguls, researchers and curious humans alike to peer into the future—provided, of course, the future doesn’t mind being stared at.
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3. Evolving Predictive Analytics in Healthcare: New AI Techniques for Real-time Interventions
2022 by Abhishek Kumar, Ashutosh Kumar Dubey, Surbhi Bhatia, Swarn Avinash Kumar, Dac-Nhuong Le
"This is the story of predictive analytics in healthcare, a field that, much like intergalactic hitchhiking, involves a lot of improbable things happening all at once, usually involving algorithms that can spot a worrisome lump or an existential crisis in a CT scan faster than you can say 'machine learning.' With the combined wizardry of AI, IoT (or the Internet of Things That Go Beep in the Night) and enough data to make your average galaxy brain feel a bit inadequate, this book boldly ventures into the curious territory of diagnosing diseases before they even think of showing up. It’s a tale of machines, humans and the occasional bout of mathematical genius coming together to, quite literally, save lives in real time—or at least try to, while figuring out how to deal with that annoying Wi-Fi connection in the Internet of 'Medical' Things."
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4. Predictive Analytics Applications with WEKA
2021 by Shuzlina Abdul Rahman & Sofianita Mutalib
In a universe where data doesn’t just exist but insists on being mined, sorted and occasionally cajoled into making sense, Predictive Analytics Applications with WEKA lands like a spaceship full of curiosity and unreasonably intelligent algorithms. Written with the kind of engaging practicality that feels like chatting with a particularly helpful robot, this book explores the wonders of WEKA—an open-source machine-learning toolkit that’s as friendly as it is clever. It leads you, step by step (because teleporting apparently wasn’t an option), through the delightful chaos of data preparation, cleansing, modeling and evaluation, complete with checklists and reassuring nods to common mistakes. With three lively case studies to showcase WEKA’s talents, this guide invites anyone, whether mildly math-phobic or algorithm-curious, to take their first leap into predictive analytics. Think of it as hitchhiking across the galaxy of data science, with WEKA as your improbably reliable guide.
Download PDF
5. Applying Predictive Analytics: Finding Value in Data
2019 by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
In the vast, swirling cosmos of business quandaries, where data points zoom about like caffeinated electrons and problems stubbornly refuse to solve themselves, Applying Predictive Analytics comes hurtling in like a spaceship piloted by improbably well-organized statisticians. This charmingly pragmatic guide takes the baffling intricacies of predictive modeling and explains them with the casual confidence of someone who’s already had tea with an algorithm. Armed with SAS Enterprise Miner and a selection of case studies so real they might as well have their own desk in your office, the authors deftly transform intimidating math into a practical toolkit for making your business outcomes less disastrous—or even, improbably, quite brilliant. Each chapter starts with a vignette that’s not just engaging but oddly reassuring, like the galaxy’s best startup founder telling you, "Don’t panic."
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6. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
2016 by Eric Siegel
In the gloriously mind-bending tradition of asking "why" far too often and "how" far too late, Predictive Analytics by Eric Siegel dives headfirst into the bewildering world where machines not only think they know you better than your mum but might also be slightly better at it. Siegel gleefully unravels the peculiar wizardry behind predicting everything from your shopping spree to your untimely demise (no, really), explaining how a bizarre cocktail of math, data and a touch of sorcery powers everything from counterterrorism to why your boss looks twitchy before layoffs. With examples as deliciously odd as Target predicting pregnancies and IBM’s Watson showing off on Jeopardy!, this book is a whimsical, slightly terrifying guide to a universe where the machines don’t just play chess—they play you, too.
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7. Predictive Analytics for Human Resources
2014 by Jac Fitz-enz, John Mattox, II
In a universe where HR departments wrestle with spreadsheets and data points lurk in the shadows waiting to be deciphered, *Predictive Analytics for Human Resources* emerges like a Vogon constructor fleet—unexpected, formidable and surprisingly helpful if you don’t panic. This isn’t just a book; it’s a galactic guide to making sense of the infinite improbability of workforce metrics. Starting with the simple (yet deceptively confounding) concept of predictive analytics, it whisks you away on a statistical safari complete with case studies that are less about numbers and more about revealing the hidden patterns behind why Bob from Accounting keeps quitting. Along the way, it dives into the esoteric arts of internal coaching, mentoring and the mysterious alchemy of recruiting sponsors—offering step-by-step instructions that rival even the most pedantic of Deep Thought’s calculations. By the end, you’ll be armed with ten essential steps for turning your analytics function into a hyper-intelligent HR Hitchhiker's Guide, capable of linking staffing woes to financial objectives in ways that will make your CFO weep with joy. Bring a towel and your curiosity; you’re going to need both.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Artificial Intelligence for Business Analytics: Algorithms, Platforms and Application Scenarios
2023 by Felix Weber
Imagine, if you will, a universe where businesses, drowning in oceans of data, cry out for a savior—not in the form of a caped hero, but a glittering beacon of Artificial Intelligence. Artificial Intelligence for Business Analytics isn’t just a book; it’s a galactic hitchhiker’s guide to surviving the bewildering cosmos of algorithms, platforms and applications. With a blend of machine learning wizardry, actionable business scenarios and a Business Analytics Model for AI that’s as reassuring as a towel in a crisis, Felix Weber takes you on a whirlwind tour of how to make sense of your data before it swallows you whole. Think predictive insights, automated decisions and AI magic—all served up with the stark realization that clinging to traditional business intelligence is about as useful as a broken-down hyperspace drive. Embrace it, or be left behind with the Vogons.
Download PDF
2. The Power of AI Prediction
2023 by HUATENG OU
In a universe teetering on the edge of chaos (or at least a slightly disorganized filing cabinet), The Power of AI Prediction cheerfully unpacks the mind-boggling brilliance of artificial intelligence, which, it turns out, is less about murderous robots and more about politely foreseeing what you might want for breakfast. HUATENG OU serves up a delightful exploration of how AI isn’t just predicting trends—it’s practically rolling out the red carpet for tomorrow. With real-world examples that are almost too clever to be true and an enthusiasm for transforming society that’s so infectious you might want to wear gloves, this book invites business moguls, researchers and curious humans alike to peer into the future—provided, of course, the future doesn’t mind being stared at.
Download PDF
3. Evolving Predictive Analytics in Healthcare: New AI Techniques for Real-time Interventions
2022 by Abhishek Kumar, Ashutosh Kumar Dubey, Surbhi Bhatia, Swarn Avinash Kumar, Dac-Nhuong Le
"This is the story of predictive analytics in healthcare, a field that, much like intergalactic hitchhiking, involves a lot of improbable things happening all at once, usually involving algorithms that can spot a worrisome lump or an existential crisis in a CT scan faster than you can say 'machine learning.' With the combined wizardry of AI, IoT (or the Internet of Things That Go Beep in the Night) and enough data to make your average galaxy brain feel a bit inadequate, this book boldly ventures into the curious territory of diagnosing diseases before they even think of showing up. It’s a tale of machines, humans and the occasional bout of mathematical genius coming together to, quite literally, save lives in real time—or at least try to, while figuring out how to deal with that annoying Wi-Fi connection in the Internet of 'Medical' Things."
Download PDF
4. Predictive Analytics Applications with WEKA
2021 by Shuzlina Abdul Rahman & Sofianita Mutalib
In a universe where data doesn’t just exist but insists on being mined, sorted and occasionally cajoled into making sense, Predictive Analytics Applications with WEKA lands like a spaceship full of curiosity and unreasonably intelligent algorithms. Written with the kind of engaging practicality that feels like chatting with a particularly helpful robot, this book explores the wonders of WEKA—an open-source machine-learning toolkit that’s as friendly as it is clever. It leads you, step by step (because teleporting apparently wasn’t an option), through the delightful chaos of data preparation, cleansing, modeling and evaluation, complete with checklists and reassuring nods to common mistakes. With three lively case studies to showcase WEKA’s talents, this guide invites anyone, whether mildly math-phobic or algorithm-curious, to take their first leap into predictive analytics. Think of it as hitchhiking across the galaxy of data science, with WEKA as your improbably reliable guide.
Download PDF
5. Applying Predictive Analytics: Finding Value in Data
2019 by Richard V. McCarthy, Mary M. McCarthy, Wendy Ceccucci, Leila Halawi
In the vast, swirling cosmos of business quandaries, where data points zoom about like caffeinated electrons and problems stubbornly refuse to solve themselves, Applying Predictive Analytics comes hurtling in like a spaceship piloted by improbably well-organized statisticians. This charmingly pragmatic guide takes the baffling intricacies of predictive modeling and explains them with the casual confidence of someone who’s already had tea with an algorithm. Armed with SAS Enterprise Miner and a selection of case studies so real they might as well have their own desk in your office, the authors deftly transform intimidating math into a practical toolkit for making your business outcomes less disastrous—or even, improbably, quite brilliant. Each chapter starts with a vignette that’s not just engaging but oddly reassuring, like the galaxy’s best startup founder telling you, "Don’t panic."
Download PDF
6. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
2016 by Eric Siegel
In the gloriously mind-bending tradition of asking "why" far too often and "how" far too late, Predictive Analytics by Eric Siegel dives headfirst into the bewildering world where machines not only think they know you better than your mum but might also be slightly better at it. Siegel gleefully unravels the peculiar wizardry behind predicting everything from your shopping spree to your untimely demise (no, really), explaining how a bizarre cocktail of math, data and a touch of sorcery powers everything from counterterrorism to why your boss looks twitchy before layoffs. With examples as deliciously odd as Target predicting pregnancies and IBM’s Watson showing off on Jeopardy!, this book is a whimsical, slightly terrifying guide to a universe where the machines don’t just play chess—they play you, too.
Download PDF
7. Predictive Analytics for Human Resources
2014 by Jac Fitz-enz, John Mattox, II
In a universe where HR departments wrestle with spreadsheets and data points lurk in the shadows waiting to be deciphered, *Predictive Analytics for Human Resources* emerges like a Vogon constructor fleet—unexpected, formidable and surprisingly helpful if you don’t panic. This isn’t just a book; it’s a galactic guide to making sense of the infinite improbability of workforce metrics. Starting with the simple (yet deceptively confounding) concept of predictive analytics, it whisks you away on a statistical safari complete with case studies that are less about numbers and more about revealing the hidden patterns behind why Bob from Accounting keeps quitting. Along the way, it dives into the esoteric arts of internal coaching, mentoring and the mysterious alchemy of recruiting sponsors—offering step-by-step instructions that rival even the most pedantic of Deep Thought’s calculations. By the end, you’ll be armed with ten essential steps for turning your analytics function into a hyper-intelligent HR Hitchhiker's Guide, capable of linking staffing woes to financial objectives in ways that will make your CFO weep with joy. Bring a towel and your curiosity; you’re going to need both.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
