Download or read book Hands On Salesforce Einstein Studio and GPT Intelligence written by Joseph Kubon and published by Packt Publishing Ltd. This book was released on 2024-09-27 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prepare for the future of CRM with the first exclusive guide to optimizing Salesforce automations with Einstein Copilots Key Features Gain essential insights to seamlessly transition from traditional model to AI-driven models and optimize CRM workflows Configure and integrate AI tools with various Salesforce components to achieve enhanced functionality Learn from seasoned Salesforce experts to drive business growth and improve customer experiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionSalesforce continues to expand the capabilities of Einstein Copilot, ensuring its ability to meet evolving critical business needs. This definitive guide to implementing Salesforce Einstein Copilot is written by a team of highly experienced Salesforce professionals with decades of experience in AI, data engineering, and Salesforce solutions. The book showcases practical use cases and offers strategic insights into AI integration within CRM systems, providing you with a comprehensive understanding of how to leverage this powerful tool. You’ll develop a thorough understanding of various use cases and practical applications of Einstein Copilot across different Salesforce clouds, including Sales, Service, Marketing, and Commerce. Equipped with strategic insights from seasoned Salesforce experts, you’ll be prepared to navigate the future of AI-driven CRM, enhancing your ability to drive productivity and innovation within your organization. Ultimately, you’ll become well versed in the transformative potential of AI, ready to harness its power to achieve superior business outcomes. By the end of this book, you’ll be able to effectively implement Salesforce Einstein Copilot within your CRM systems, leveraging AI to optimize information and streamline business processes. What you will learn Use Prompt Builder, Model Builder, and Copilot Actions to drive enhanced productivity for sales and service teams Create and manage effective user prompts to streamline interactions Implement and customize Copilot Actions to automate complex workflows and improve efficiency Develop, train, and deploy custom AI models with Model Builder to address specific business needs Centralize and harmonize customer data using Data Cloud to gain unified insights Tailor Copilot's features to meet unique business requirements, ensuring maximum relevance and effectiveness Who this book is for This book is for Salesforce professionals, including administrators, developers, and consultants, who want to leverage their Salesforce skills using AI to optimize business processes, enhance customer experiences, and drive growth.

Download or read book Learning Einstein Analytics written by santosh chitalkar and published by Packt Publishing Ltd. This book was released on 2018-01-29 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to confidently setup and create app, lenses, dashboards using Salesforce Einstein Analytics. Key Features Explore Einstein analytics on desktop as well as mobile platforms Turn data into smarter sales with Einstein Analytics for Sales Visualize your data with preloaded as well as customized dashboards Book Description Salesforce Einstein analytics aka Wave Analytics is a cloud-based platform which connects data from the multiple sources and explores it to uncover insights. It empowers sales reps, marketers, and analysts with the insights to make customer interactions smarter, without building mathematical models. You will learn to create app, lenses, dashboards and share dashboards with other users. This book starts off with explaining you fundamental concepts like lenses, step, measures and sets you up with Einstein Analytics platform. We then move on to creating an app and here you will learn to create datasets, dashboards and different ways to import data into Analytics. Moving on we look at Einstein for sales, services, and marketing individually. Here you will learn to manage your pipeline, understand important business drivers and visualize trends. You will also learn features related to data monitoring tools and embedding dashboards with lightning, visualforce page and mobile devices. Further, you will learn advanced features pertaining to recent advancements in Einstein which include machine learning constructs and getting predictions for events. By the end of this book, you will become proficient in the Einstein analytics, getting insights faster and understanding your customer in a better way. What you will learn Create app, lenses, and dashboards using Einstein. Visualize data utilizing all the widgets available with Einstein. Understand Einstein for Sales, Service, and Marketing separately. Use Data monitoring tools to monitor data flow and system jobs. Abstract machine learning constructs and make predictions on events Who this book is for This book is for data scientists, business users, developers who want to explore business data using the Salesforce Einstein Analytics. Knowledge of the Salesforce platform is required.

Download or read book Learning Salesforce Einstein written by Mohith Shrivastava and published by Packt Publishing Ltd. This book was released on 2017-06-28 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporate the power of Einstein in your Salesforce application About This Book Make better predictions of your business processes using prediction and predictive modeling Build your own custom models by leveraging PredictionIO on the Heroku platform Integrate Einstein into various cloud services to predict sales, marketing leads, insights into news feeds, and more Who This Book Is For This book is for developers, data scientists, and Salesforce-experienced consultants who want to explore Salesforce Einstein and its current offerings. It assumes some prior experience with the Salesforce platform. What You Will Learn Get introduced to AI and its role in CRM and cloud applications Understand how Einstein works for the sales, service, marketing, community, and commerce clouds Gain a deep understanding of how to use Einstein for the analytics cloud Build predictive apps on Heroku using PredictionIO, and work with Einstein Predictive Vision Services Incorporate Einstein in the IoT cloud Test the accuracy of Einstein through Salesforce reporting and Wave analytics In Detail Dreamforce 16 brought forth the latest addition to the Salesforce platform: an AI tool named Einstein. Einstein promises to provide users of all Salesforce applications with a powerful platform to help them gain deep insights into the data they work on. This book will introduce you to Einstein and help you integrate it into your respective business applications based on the Salesforce platform. We start off with an introduction to AI, then move on to look at how AI can make your CRM and apps smarter. Next, we discuss various out-of-the-box components added to sales, service, marketing, and community clouds from salesforce to add Artificial Intelligence capabilities. Further on, we teach you how to use Heroku, PredictionIO, and the force.com platform, along with Einstein, to build smarter apps. The core chapters focus on developer content and introduce PredictionIO and Salesforce Einstein Vision Services. We explore Einstein Predictive Vision Services, along with analytics cloud, the Einstein Data Discovery product, and IOT core concepts. Throughout the book, we also focus on how Einstein can be integrated into CRM and various clouds such as sales, services, marketing, and communities. By the end of the book, you will be able to embrace and leverage the power of Einstein, incorporating its functions to gain more knowledge. Salesforce developers will be introduced to the world of AI, while data scientists will gain insights into Salesforce's various cloud offerings and how they can use Einstein's capabilities and enhance applications. Style and approach This book takes a straightforward approach to explain Salesforce Einstein and all of its potential applications. Filled with examples, the book presents the facts along with seasoned advice and real-world use cases to ensure you have all the resources you need to incorporate the power of Einstein in your work.

Download or read book Getting Started with Salesforce Einstein Analytics written by Johan Yu and published by Apress. This book was released on 2019-09-11 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build interactive dashboards using Salesforce Einstein analytics. Explore all of your data quickly and easily by providing AI-powered advanced analytics, right in Salesforce. You will manage datasets, query data with Salesforce Analytics Query Language (SAQL), and customize dashboards. Because Einstein Analytics is new, the curve to learn this technology can be difficult. This book guides you step-by-step in simple, easy-to-understand terms to get data from the Salesforce platform to the Einstein Analytics platform and also shows you how to import external data (e.g., CSV files). Core chapters focus on understanding data sources, dataflow, dataset, and lens leading up to building dashboards from scratch. Advanced features such as data transformation using computeExpression and computeRelative as well as dataflow with a multi-value lookup are explored. What You Will Learn Use data from Salesforce and external sources Create a dataflow to build a flexible datasetBuild dashboards using Einstein Analytics Explore and analyze data using Einstein Analytics Utilize SAQL and binding to create advance dashboards Who This Book Is For IT users getting started with Einstein Analytics, Salesforce consultants starting new Einstein Analytics projects, and power users familiar with Salesforce reporting and dashboards who want to get up to speed on new analytics features

Download or read book Creating Actionable Insights Using CRM Analytics written by Mark Tossell and published by Packt Publishing Ltd. This book was released on 2021-12-29 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage CRM Analytics to generate valuable business insights and solve business problems efficiently Key FeaturesExtract, combine, transform, and visualize your data to derive business insights using CRM AnalyticsGain hands-on experience as you walk through practical dashboard use cases in CRM AnalyticsLearn how to build best-in-class dashboards from a CRM Analytics thought leaderBook Description CRM Analytics, formerly known as Tableau CRM and Einstein Analytics, is a powerful and versatile data analytics platform that enables organizations to extract, combine, transform, and visualize their data to create valuable business insights. Creating Actionable Insights Using CRM Analytics provides a hands-on approach to CRM Analytics implementation and associated methodologies that will have you up and running and productive in no time. The book provides you with detailed explanations of essential concepts to help you to gain confidence and become competent in using the CRM Analytics platform for data extraction, combination, transformation, visualization, and action. As you make progress, you'll understand what CRM Analytics is and where it provides business value. You'll also learn how to bring your data together in CRM Analytics, build datasets and lenses for data analysis, create effective analytics dashboards for visualization and consumption by end users, and build dashboard actions that take the user from data to insight to action with ease. By the end of this book, you'll be able to solve business problems using CRM Analytics and design, build, test, and deploy analytics dashboards efficiently. What you will learnImplement and configure CRM Analytics from scratchBuild your first CRM Analytics app and embed your CRM Analytics dashboards in Salesforce to enhance user adoptionConnect Salesforce and external data with CRM Analytics and create datasetsCreate a data recipe and get familiar with the recipe UIBuild a custom dashboard in CRM Analytics using the dashboard editorUse lenses to create a CRM Analytics dashboardConfigure and implement data security and governanceBuild configured record actions to automate data directly in SalesforceWho this book is for This book is for data analysts, business analysts, BI professionals, and Salesforce users who want to explore CRM Analytics' capabilities and features. Basic knowledge of Salesforce and data analytics is assumed to get the most out of this book.

Download or read book Love and Work written by Marcus Buckingham and published by Harvard Business Press. This book was released on 2022-04-05 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Wall Street Journal bestseller World-renowned researcher and New York Times bestselling author Marcus Buckingham helps us discover where we're at our best—both at work and in life. You've long been told to "Do what you love." Sounds simple, but the real challenge is how to do this in a world not set up to help you. Most of us actually don't know the real truth of what we love—what engages us and makes us thrive—and our workplaces, jobs, schools, even our parents, are focused instead on making us conform. Sadly, no person or system is dedicated to discovering the crucial intersection between what you love to do and how you contribute it to others. In this eye-opening, uplifting book, Buckingham shows you how to break free from this conformity—how to decode your own loves, turn them into their most powerful expression, and do the same for those you lead and those you love. How can you use love to reveal your unique gifts? How can you pinpoint what makes you stand out from anyone else? How can you choose roles in which you'll excel? Love and Work unlocks answers to these questions and others, so you can: Choose the right role on the team. Describe yourself compellingly in job interviews. Mold your existing role so that it calls upon the very best of you. Position yourself as a leader in such a way that your followers quickly come to trust in you. Make lasting change for your team, your company, your family, or your students. Love, the most powerful of human emotions, the source of all creativity, collaboration, insight, and excellence, has been systematically drained from our lives—our work, teams, and classrooms. It's time we brought love back in. Love and Work shows you how.

Download or read book Marketing Artificial Intelligence written by Paul Roetzer and published by BenBella Books. This book was released on 2022-06-28 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence is forecasted to have trillions of dollars of impact on businesses and the economy, yet many marketers struggle to understand what it is and how to apply it in their marketing efforts. The truth is, AI possesses the power to change everything. While AI-powered marketing technologies may never achieve the sci-fi vision of self-running, self-improving autonomous systems, a little bit of AI can go a long way toward dramatically increasing productivity, efficiency, and performance. Marketing AI Institute’s Founder & CEO, Paul Roetzer, and Chief Content Officer, Mike Kaput, join forces to show marketers how to embrace AI and make it their competitive advantage. Marketing Artificial Intelligence draws on years of research and dozens of interviews with AI marketers, executives, engineers, and entrepreneurs. Roetzer and Kaput present the current potential of AI, as well as a glimpse into a near future in which marketers and machines work seamlessly to run personalized campaigns of unprecedented complexity with unimaginable simplicity. As the amount of data exponentially increases, marketers’ abilities to filter through the noise and turn information into actionable intelligence remain limited. Roetzer and Kaput show you how to make breaking through that noise your superpower. So, come along on a journey of exploration and enlightenment. Marketing Artificial Intelligence is the blueprint for understanding and applying AI, giving you just the edge in your career you’ve been waiting for.

Download or read book Automated Machine Learning written by Adnan Masood and published by Packt Publishing Ltd. This book was released on 2021-02-18 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP, or any platform of your choiceEliminate mundane tasks in data engineering and reduce human errors in machine learning modelsFind out how you can make machine learning accessible for all users to promote decentralized processesBook Description Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort. This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and much more. You'll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle. By the end of this machine learning book, you’ll be able to build and deploy AutoML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks. What you will learnExplore AutoML fundamentals, underlying methods, and techniquesAssess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an applied scenarioFind out the difference between cloud and operations support systems (OSS)Implement AutoML in enterprise cloud to deploy ML models and pipelinesBuild explainable AutoML pipelines with transparencyUnderstand automated feature engineering and time series forecastingAutomate data science modeling tasks to implement ML solutions easily and focus on more complex problemsWho this book is for Citizen data scientists, machine learning developers, artificial intelligence enthusiasts, or anyone looking to automatically build machine learning models using the features offered by open source tools, Microsoft Azure Machine Learning, AWS, and Google Cloud Platform will find this book useful. Beginner-level knowledge of building ML models is required to get the best out of this book. Prior experience in using Enterprise cloud is beneficial.

Download or read book Artificial Intelligence Basics written by Tom Taulli and published by Apress. This book was released on 2019-08-01 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence touches nearly every part of your day. While you may initially assume that technology such as smart speakers and digital assistants are the extent of it, AI has in fact rapidly become a general-purpose technology, reverberating across industries including transportation, healthcare, financial services, and many more. In our modern era, an understanding of AI and its possibilities for your organization is essential for growth and success. Artificial Intelligence Basics has arrived to equip you with a fundamental, timely grasp of AI and its impact. Author Tom Taulli provides an engaging, non-technical introduction to important concepts such as machine learning, deep learning, natural language processing (NLP), robotics, and more. In addition to guiding you through real-world case studies and practical implementation steps, Taulli uses his expertise to expand on the bigger questions that surround AI. These include societal trends, ethics, and future impact AI will have on world governments, company structures, and daily life. Google, Amazon, Facebook, and similar tech giants are far from the only organizations on which artificial intelligence has had—and will continue to have—an incredibly significant result. AI is the present and the future of your business as well as your home life. Strengthening your prowess on the subject will prove invaluable to your preparation for the future of tech, and Artificial Intelligence Basics is the indispensable guide that you’ve been seeking. What You Will Learn Study the core principles for AI approaches such as machine learning, deep learning, and NLP (Natural Language Processing)Discover the best practices to successfully implement AI by examining case studies including Uber, Facebook, Waymo, UiPath, and Stitch FixUnderstand how AI capabilities for robots can improve businessDeploy chatbots and Robotic Processing Automation (RPA) to save costs and improve customer serviceAvoid costly gotchasRecognize ethical concerns and other risk factors of using artificial intelligenceExamine the secular trends and how they may impact your business Who This Book Is For Readers without a technical background, such as managers, looking to understand AI to evaluate solutions.

Download or read book Machine Learning Design Patterns written by Valliappa Lakshmanan and published by O'Reilly Media. This book was released on 2020-10-15 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Download or read book The Cultural Life of Machine Learning written by Jonathan Roberge and published by Springer Nature. This book was released on 2020-11-30 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the work of historians and sociologists with perspectives from media studies, communication studies, cultural studies, and information studies to address the origins, practices, and possible futures of contemporary machine learning. From its foundations in 1950s and 1960s pattern recognition and neural network research to the modern-day social and technological dramas of DeepMind’s AlphaGo, predictive political forecasting, and the governmentality of extractive logistics, machine learning has become controversial precisely because of its increased embeddedness and agency in our everyday lives. How can we disentangle the history of machine learning from conventional histories of artificial intelligence? How can machinic agents’ capacity for novelty be theorized? Can reform initiatives for fairness and equity in AI and machine learning be realized, or are they doomed to cooptation and failure? And just what kind of “learning” does machine learning truly represent? We empirically address these questions and more to provide a baseline for future research. Chapter 2 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Download or read book Banking 5 0 written by Bernardo Nicoletti and published by Springer Nature. This book was released on 2021-07-06 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bill Gates’ quote, “Banking is necessary, but banks are not,” showcases the opportunity for financial services digital transformation. The next transition from industry 4.0 to 5.0 will impact all sectors, including banking. It will combine information technology and automation, based on artificial intelligence, person-robot collaboration, and sustainability. It is time to analyze this transformation in banking deeply, so that the sector can adequately change to the ‘New Normal’ and a wholly modified banking model can be properly embedded in the business. This book presents a conceptual model of banking 5.0, detailing its implementation in processes, platforms, people, and partnerships of financial services organizations companies. The last part of the book is then dedicated to future developments. Of interest to academics, researchers, and professionals in banking, financial technology, and financial services, this book also includes business cases in financial services.

Download or read book The Hundred page Machine Learning Book written by Andriy Burkov and published by . This book was released on 2019 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.

Download or read book China s Crisis of Success written by William H. Overholt and published by Cambridge University Press. This book was released on 2018-01-11 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: China's Crisis of Success provides new perspectives on China's rise to superpower status, showing that China has reached a threshold where success has eliminated the conditions that enabled miraculous growth. Continued success requires re-invention of its economy and politics. The old economic strategy based on exports and infrastructure now piles up debt without producing sustainable economic growth, and Chinese society now resists the disruptive change that enabled earlier reforms. While China's leadership has produced a strategy for successful economic transition, it is struggling to manage the politics of implementing that strategy. After analysing the economics of growth, William H. Overholt explores critical social issues of the transition, notably inequality, corruption, environmental degradation, and globalisation. He argues that Xi Jinping is pursuing the riskiest political strategy of any important national leader. Alternative outcomes include continued impressive growth and political stability, Japanese-style stagnation, and a major political-economic crisis.

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.

Download or read book Learning Apex Programming written by Matt Kaufman and published by Packt Publishing Ltd. This book was released on 2015-01-31 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are a developer who has some object-oriented programming experience, Learning Apex Programming is the perfect book for you. This book is most appropriate for developers who wish to gain an understanding of the Force.com platform and how to use Apex to create business applications.

Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets