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Summary The Strategy Machine
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Book Synopsis Summary: The Strategy Machine by : BusinessNews Publishing,
Download or read book Summary: The Strategy Machine written by BusinessNews Publishing, and published by Primento. This book was released on 2014-11-12 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: The must-read summary of Larry Downes' book: "The Strategy Machine: Building Your Business One Idea at a Time". This complete summary of the ideas from Larry Downes' book "The Strategy Machine" shows that disposable computers are set to transform every industry. To be ready for this, the ultimate extension of the Information Revolution, you need to develop a “Strategy Machine” which incorporates a portfolio of strategies that you can use to harness these imminent and dramatic changes in the marketplace. In his book, the author explains that a viable and effective strategy machine will have a portfolio with three types of investments: projects, venture and options. This summary describes each of these investment types in detail and shows how you can use them to move forward in the Information Revolution. Added-value of this summary: • Save time • Understand key concepts • Expand your business knowledge To learn more, read "The Strategy Machine" and discover this essential guidebook that provides essential tools for re-thinking your company's strategy portfolio.
Book Synopsis Strategic Computing by : Alex Roland
Download or read book Strategic Computing written by Alex Roland and published by MIT Press. This book was released on 2002 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The story of the U.S. Department of Defense's extraordinary effort, in the period from 1983 to 1993, to achieve machine intelligence.
Download or read book Radically Human written by Paul Daugherty and published by Harvard Business Press. This book was released on 2022-04-26 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology advances are making tech more . . . human. This changes everything you thought you knew about innovation and strategy. In their groundbreaking book, Human + Machine, Accenture technology leaders Paul R. Daugherty and H. James Wilson showed how leading organizations use the power of human-machine collaboration to transform their processes and their bottom lines. Now, as new AI powered technologies like the metaverse, natural language processing, and digital twins begin to rapidly impact both life and work, those companies and other pioneers across industries are tipping the balance even more strikingly toward the human side with technology-led strategy that is reshaping the very nature of innovation. In Radically Human, Daugherty and Wilson show this profound shift, fast-forwarded by the pandemic, toward more human—and more humane—technology. Artificial intelligence is becoming less artificial and more intelligent. Instead of data-hungry approaches to AI, innovators are pursuing data-efficient approaches that enable machines to learn as humans do. Instead of replacing workers with machines, they're unleashing human expertise to create human-centered AI. In place of lumbering legacy IT systems, they're building cloud-first IT architectures able to continuously adapt to a world of billions of connected devices. And they're pursuing strategies that will take their place alongside classic, winning business formulas like disruptive innovation. These against-the-grain approaches to the basic building blocks of business—Intelligence, Data, Expertise, Architecture, and Strategy (IDEAS)—are transforming competition. Industrial giants and startups alike are drawing on this radically human IDEAS framework to create new business models, optimize post-pandemic approaches to work and talent, rebuild trust with their stakeholders, and show the way toward a sustainable future. With compelling insights and fresh examples from a variety of industries, Radically Human will forever change the way you think about, practice, and win with innovation.
Book Synopsis The Ultimate Sales Machine by : Chet Holmes
Download or read book The Ultimate Sales Machine written by Chet Holmes and published by Penguin. This book was released on 2007-06-21 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: NEWLY REVISED AND UPDATED The bestselling business playbook for turbocharging any organization, updated for modern audiences with new and never-before-seen material Every single day 3,076 businesses shut their doors. But what if you could create the finest, most profitable and best-run version of your business without wasting precious dollars on a thousand different strategies? When The Ultimate Sales Machine first published in 2007, legendary sales expert Chet Holmes gave us the key to do just that. All you need is to focus on twelve key areas of improvement—and practice them over and over with pigheaded discipline. Now, a decade later, Chet’s daughter Amanda Holmes breathes new life into her father’s classic advice. With updated language to match our ever-changing times and over 50 new pages of content, The Ultimate Sales Machine will help any modern reader transform their organization into a high-performing, moneymaking force. With practical tools, real-life examples, and proven strategies, this book will show you how to: • Teach your team to work smarter, not harder • Get more bang from your marketing for less • Perfect every sales interaction by working on sales, not just in sales • Land your dream clients This revised edition expands on these proven concepts, with checklists to get faster ROIs, Core Story Frameworks to get your company to number one in your marketplace, and a bonus, never-before-revealed chapter from Chet, “How to Live a Rich and Full Life,” that will put you in the best possible mindset to own your career. For every CEO, manager, and business owner who wants to take their organization to the next level, The Ultimate Sales Machine will put you and your company on the path to success—and help you stay there!
Book Synopsis The AI Marketing Canvas by : Raj Venkatesan
Download or read book The AI Marketing Canvas written by Raj Venkatesan and published by Stanford University Press. This book was released on 2021-05-18 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a direct, actionable plan CMOs can use to map out initiatives that are properly sequenced and designed for success—regardless of where their marketing organization is in the process. The authors pose the following critical questions to marketers: (1) How should modern marketers be thinking about artificial intelligence and machine learning? and (2) How should marketers be developing a strategy and plan to implement AI into their marketing toolkit? The opening chapters provide marketing leaders with an overview of what exactly AI is and how is it different than traditional computer science approaches. Venkatesan and Lecinski, then, propose a best-practice, five-stage framework for implementing what they term the "AI Marketing Canvas." Their approach is based on research and interviews they conducted with leading marketers, and offers many tangible examples of what brands are doing at each stage of the AI Marketing Canvas. By way of guidance, Venkatesan and Lecinski provide examples of brands—including Google, Lyft, Ancestry.com, and Coca-Cola—that have successfully woven AI into their marketing strategies. The book concludes with a discussion of important implications for marketing leaders—for your team and culture.
Book Synopsis Human + Machine by : Paul R. Daugherty
Download or read book Human + Machine written by Paul R. Daugherty and published by Harvard Business Press. This book was released on 2018-03-20 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI is radically transforming business. Are you ready? Look around you. Artificial intelligence is no longer just a futuristic notion. It's here right now--in software that senses what we need, supply chains that "think" in real time, and robots that respond to changes in their environment. Twenty-first-century pioneer companies are already using AI to innovate and grow fast. The bottom line is this: Businesses that understand how to harness AI can surge ahead. Those that neglect it will fall behind. Which side are you on? In Human + Machine, Accenture leaders Paul R. Daugherty and H. James (Jim) Wilson show that the essence of the AI paradigm shift is the transformation of all business processes within an organization--whether related to breakthrough innovation, everyday customer service, or personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or to completely reimagine them. AI is changing all the rules of how companies operate. Based on the authors' experience and research with 1,500 organizations, the book reveals how companies are using the new rules of AI to leap ahead on innovation and profitability, as well as what you can do to achieve similar results. It describes six entirely new types of hybrid human + machine roles that every company must develop, and it includes a "leader’s guide" with the five crucial principles required to become an AI-fueled business. Human + Machine provides the missing and much-needed management playbook for success in our new age of AI. BOOK PROCEEDS FOR THE AI GENERATION The authors' goal in publishing Human + Machine is to help executives, workers, students and others navigate the changes that AI is making to business and the economy. They believe AI will bring innovations that truly improve the way the world works and lives. However, AI will cause disruption, and many people will need education, training and support to prepare for the newly created jobs. To support this need, the authors are donating the royalties received from the sale of this book to fund education and retraining programs focused on developing fusion skills for the age of artificial intelligence.
Book Synopsis Competing in the Age of AI by : Marco Iansiti
Download or read book Competing in the Age of AI written by Marco Iansiti and published by Harvard Business Press. This book was released on 2020-01-07 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: "a provocative new book" — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI.
Download or read book The Big Nine written by Amy Webb and published by PublicAffairs. This book was released on 2019-03-05 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: A call-to-arms about the broken nature of artificial intelligence, and the powerful corporations that are turning the human-machine relationship on its head. We like to think that we are in control of the future of "artificial" intelligence. The reality, though, is that we -- the everyday people whose data powers AI -- aren't actually in control of anything. When, for example, we speak with Alexa, we contribute that data to a system we can't see and have no input into -- one largely free from regulation or oversight. The big nine corporations -- Amazon, Google, Facebook, Tencent, Baidu, Alibaba, Microsoft, IBM and Apple--are the new gods of AI and are short-changing our futures to reap immediate financial gain. In this book, Amy Webb reveals the pervasive, invisible ways in which the foundations of AI -- the people working on the system, their motivations, the technology itself -- is broken. Within our lifetimes, AI will, by design, begin to behave unpredictably, thinking and acting in ways which defy human logic. The big nine corporations may be inadvertently building and enabling vast arrays of intelligent systems that don't share our motivations, desires, or hopes for the future of humanity. Much more than a passionate, human-centered call-to-arms, this book delivers a strategy for changing course, and provides a path for liberating us from algorithmic decision-makers and powerful corporations.
Book Synopsis Your Strategy Needs a Strategy by : Martin Reeves
Download or read book Your Strategy Needs a Strategy written by Martin Reeves and published by Harvard Business Review Press. This book was released on 2015-05-19 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: You think you have a winning strategy. But do you? Executives are bombarded with bestselling ideas and best practices for achieving competitive advantage, but many of these ideas and practices contradict each other. Should you aim to be big or fast? Should you create a blue ocean, be adaptive, play to win—or forget about a sustainable competitive advantage altogether? In a business environment that is changing faster and becoming more uncertain and complex almost by the day, it’s never been more important—or more difficult—to choose the right approach to strategy. In this book, The Boston Consulting Group’s Martin Reeves, Knut Haanæs, and Janmejaya Sinha offer a proven method to determine the strategy approach that is best for your company. They start by helping you assess your business environment—how unpredictable it is, how much power you have to change it, and how harsh it is—a critical component of getting strategy right. They show how existing strategy approaches sort into five categories—Be Big, Be Fast, Be First, Be the Orchestrator, or simply Be Viable—depending on the extent of predictability, malleability, and harshness. In-depth explanations of each of these approaches will provide critical insight to help you match your approach to strategy to your environment, determine when and how to execute each one, and avoid a potentially fatal mismatch. Addressing your most pressing strategic challenges, you’ll be able to answer questions such as: • What replaces planning when the annual cycle is obsolete? • When can we—and when should we—shape the game to our advantage? • How do we simultaneously implement different strategic approaches for different business units? • How do we manage the inherent contradictions in formulating and executing different strategies across multiple businesses and geographies? Until now, no book brings it all together and offers a practical tool for understanding which strategic approach to apply. Get started today.
Book Synopsis Advances in Financial Machine Learning by : Marcos Lopez de Prado
Download or read book Advances in Financial Machine Learning written by Marcos Lopez de Prado and published by John Wiley & Sons. This book was released on 2018-01-23 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Download or read book The Goal written by Eliyahu M. Goldratt and published by Routledge. This book was released on 2016-08-12 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alex Rogo is a harried plant manager working ever more desperately to try and improve performance. His factory is rapidly heading for disaster. So is his marriage. He has ninety days to save his plant - or it will be closed by corporate HQ, with hundreds of job losses. It takes a chance meeting with a colleague from student days - Jonah - to help him break out of conventional ways of thinking to see what needs to be done. Described by Fortune as a 'guru to industry' and by Businessweek as a 'genius', Eliyahu M. Goldratt was an internationally recognized leader in the development of new business management concepts and systems. This 20th anniversary edition includes a series of detailed case study interviews by David Whitford, Editor at Large, Fortune Small Business, which explore how organizations around the world have been transformed by Eli Goldratt's ideas. The story of Alex's fight to save his plant contains a serious message for all managers in industry and explains the ideas which underline the Theory of Constraints (TOC) developed by Eli Goldratt. Written in a fast-paced thriller style, The Goal is the gripping novel which is transforming management thinking throughout the Western world. It is a book to recommend to your friends in industry - even to your bosses - but not to your competitors!
Book Synopsis Machine Learning for Algorithmic Trading by : Stefan Jansen
Download or read book Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Book Synopsis Inhuman Power by : Nick Dyer-Witheford
Download or read book Inhuman Power written by Nick Dyer-Witheford and published by Pluto Press (UK). This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past several years have brought staggering advances in the field of Artificial Intelligence. And Marxist analysis has to keep up: while machines were always central to Marxist analysis, modern AI is a new kind of machine that Marx could not have anticipated. Inhuman Power explores the relationship between Marxist theory and AI through three approaches, each using the lens of a different Marxist theoretical concept. While the idea of widespread AI tends to be celebrated as much as questioned, a deeper analysis of its reach and potential produces a more complex and disturbing picture than has been identified. Inhuman Power argues that on its current trajectory, AI is likely to render humanity obsolete and that the only way to prevent it is a communist revolution.
Book Synopsis Artificial Intelligence and Machine Learning Fundamentals by : Zsolt Nagy
Download or read book Artificial Intelligence and Machine Learning Fundamentals written by Zsolt Nagy and published by Packt Publishing Ltd. This book was released on 2018-12-12 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
Book Synopsis Artificial Intelligence and Deep Learning in Pathology by : Stanley Cohen
Download or read book Artificial Intelligence and Deep Learning in Pathology written by Stanley Cohen and published by Elsevier Health Sciences. This book was released on 2020-06-02 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Stanley Cohen covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience. Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.
Book Synopsis Hands-On Machine Learning for Algorithmic Trading by : Stefan Jansen
Download or read book Hands-On Machine Learning for Algorithmic Trading written by Stefan Jansen and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.
