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A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects Key Features Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. Description Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. What will you learn Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. Who this book is for This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. Table of Contents 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI About the Author Rehan Guha —A Researcher by the day and an Artist by night. Our Author is a Scholar -lecturer, an Innovator, and also a Humanitarian -Philanthropist. He started his life as a Coder, Developer, and now he is into research in the field of Machine Learning and Algorithms but also has a keen interest in General Science, Technology, Invention & Innovation. The author holds a graduation degree from the Institute of Engineering & Management, Kolkata, and a Postgraduate certification on Deep Learning from the Indian Institute of Technology, Kharagpur (IIT-K)-AICTE approved FDP course. If we talk about Rehan's area of interest, it lies in Optimization Problems, Explainable AI, Deep Learning Architecture, Algorithms, Complexity, Algorithmic Thinking, et cetera… He has multiple publications through Journals and Open Publications, along with his publications he has filed multiple patents for his Innovations and Inventions. At an early age, one of his patents was also demonstrated to the Indian Army. In Rehan’s career, he has been involved with a variety of Business Verticals, starting from Banking, Consulting, Law, Insurance, Freight & Logistics, and Telcom.
Get familiar with various Supervised, Unsupervised and Reinforcement learning algorithms Key FeaturesUnderstand the types of Machine learning. Get familiar with different Feature extraction methods. Get an overview of how Neural Network Algorithms work.Learn how to implement Decision Trees and Random Forests. The book not only explains the Classification algorithms but also discusses the deviations/ mathematical modeling.Description This book covers important concepts and topics in Machine Learning. It begins with Data Cleansing and presents an overview of Feature Selection. It then talks about training and testing, cross-validation, and Feature Selection. The book covers algorithms and implementations of the most common Feature Selection Techniques. The book then focuses on Linear Regression and Gradient Descent. Some of the important Classification techniques such as K-nearest neighbors, logistic regression, Naïve Bayesian, and Linear Discriminant Analysis are covered in the book. It then gives an overview of Neural Networks and explains the biological background, the limitations of the perceptron, and the backpropagation model. The Support Vector Machines and Kernel methods are also included in the book. It then shows how to implement Decision Trees and Random Forests. Towards the end, the book gives a brief overview of Unsupervised Learning. Various Feature Extraction techniques, such as Fourier Transform, STFT, and Local Binary patterns, are covered. The book also discusses Principle Component Analysis and its implementation. What will you learnLearn how to prepare Data for Machine Learning.Learn how to implement learning algorithms from scratch.Use scikit-learn to implement algorithms.Use various Feature Selection and Feature Extraction methods.Learn how to develop a Face recognition system. Who this book is for The book is designed for Undergraduate and Postgraduate Computer Science students and for the professionals who intend to switch to the fascinating world of Machine Learning. This book requires basic know-how of programming fundamentals, Python, in particular.Table of Contents 1. An introduction to Machine Learning 2. The beginning: Pre-Processing and Feature Selection 3. Regression 4. Classification 5. Neural Networks- I 6. Neural Networks-II 7. Support Vector machines 8. Decision Trees 9. Clustering 10. Feature Extraction Appendix A1. Cheat Sheets A2. Face Detection A3.Biblography About the Author Harsh Bhasin is an Applied Machine Learning researcher. Mr. Bhasin worked as Assistant Professor in Jamia Hamdard, New Delhi, and taught as a guest faculty in various institutes including Delhi Technological University. Before that, he worked in C# Client-Side Development and Algorithm Development. Mr. Bhasin has authored a few papers published in renowned journals including Soft Computing, Springer, BMC Medical Informatics and Decision Making, AI and Society, etc. He is the reviewer of prominent journals and has been the editor of a few special issues. He has been a recipient of a distinguished fellowship. Outside work, he is deeply interested in Hindi Poetry, progressive era, Hindustani Classical Music, percussion instruments. His areas of interest include Data Structures, Algorithms Analysis and Design, Theory of Computation , Python, Machine Learning and Deep learning. Your LinkedIn Profile: https://in.linkedin.com/in/harsh-bhasin-69134426
Understand the essentials of Machine Learning and its impact in financial sector Key Features Explore the spectrum of machine learning and its usage. Understand the NLP and Computer Vision and their use cases. Understand the Neural Network, CNN, RNN and their applications. Understand the Reinforcement Learning and their applications. Learn the rising application of Machine Learning in the Finance sector. Exposure to data mining, data visualization and data analytics. Description The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation. The book demonstrates how to solve some of the most common issues in the financial industry. The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Naïve Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. What will you learn You will grasp the most relevant techniques of Machine Learning for everyday use. You will be confident in building and implementing ML algorithms. Familiarize the adoption of Machine Learning for your business need. Discover more advanced concepts applied in banking and other sectors today. Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. Who this book is for Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. Table of Contents 1.Introduction 2.Naive Bayes, Normal Distribution and Automatic Clustering Processes 3.Machine Learning for Data Structuring 4.Parsing Data Using NLP 5.Computer Vision 6.Neural Network, GBM and Gradient Descent 7.Sequence Modeling 8.Reinforcement Learning For Financial Markets 9.Finance Use Cases 10.Impact of Machine Learning on Fintech 11.Machine Learning in Finance 12.eKYC and Anti-Fraud Policy 13.Uses of Data Mining and Data Visualization 14.Advantages and Disadvantages of Machine Learning 15.Applications of Machine Learning in Other Industries 16.Ethical considerations in Artificial Intelligence 17.Artificial Intelligence in Banking 18.Common Machine Learning Algorithms 19.Frequently Asked Questions About the Author SAURAV SINGLA —Saurav is a high performing Senior Data Scientist with 15 years of deep expertise in the application of analytics, business intelligence, machine learning, and statistics in multiple industries and 3 years of consulting experience and 5 years of managing a team in the data science field. He is a creative problem solver with a unique mix of technical, business, and research proficiency that lends itself to developing key strategies and solutions with a significant impact on revenue and ROI. He has working experience in machine learning, statistics, natural language processing, and deep learning with extensive use of Python, R, SQL & Tableau. LinkedIn Profile: https://www.linkedin.com/in/saurav-singla-5b412320/
Solve business problems with data-driven techniques and easy-to-follow Python examples Key Features Essential coverage on statistics and data science techniques. Exposure to Jupyter, PyCharm, and use of GitHub. Real use-cases, best practices, and smart techniques on the use of data science for data applications. Description This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. What you will learn Rapid understanding of Python concepts for data science applications. Understand and practice how to run data analysis with data science techniques and algorithms. Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. Become self-sufficient to perform data science tasks with the best tools and techniques. Who this book is for This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Table of Contents 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All Chapters’ Learning About the Author Prateek Gupta is a Data Enthusiast and loves data-driven technologies. Prateek has completed his B.Tech in Computer Science & Engineering and he is currently working as a Data Scientist in an IT company. Prateek has a total 9 years of experience in the software industry, and currently, he is working in the computer vision area. Prateek has implemented various end-to-end Data Science projects for fishing, winery, and ecommerce clients. His implemented object detection and recognition models and product recommendation engines have solved many business problems of various clients. His keen area of interest is in natural language processing and computer vision. In his leisure time, he writes posts about artificial intelligence in his blog. Blog links: http://dsbyprateekg.blogspot.com/ LinkedIn Profile: https://www.linkedin.com/in/prateek-gupta-64203354/
Covers Data Science concepts, processes, and the real-world hands-on use cases. Key Features -Covers the journey from a basic programmer to an effective Data Science developer. -Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. -Implementation of MLOps using Microsoft Azure DevOps. Description "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. What you will learn -Organize Data Science projects using CRISP-DM and Microsoft TDSP. -Learn to acquire and explore data using Python visualizations. -Get well versed with the implementation of data pre-processing and Feature Engineering. -Understand algorithm selection, model development, and model evaluation. -Hands-on with Azure ML Service, its architecture, and capabilities. -Learn to use Azure ML SDK and MLOps for implementing real-world use cases. Who this book is for This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. Table of Contents 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models
A pragmatic guide that will teach you to implement Agile, SCRUM and Kanban in your organization. Key Features Expert-guided techniques for successful Agile transformation in your organization. Solution-focused responses on interview questions of Agile SCRUM, XP, DSDM, KANBAN and SCRUMBAN. Reference guide to prepare for leading PMI-ACP and SAFe Certification exam. Description This book is for businesses that aspire to improve agility, deliver fit-for-purpose products and services, delight customers, and provide the security of long-term survival associated with mature businesses that consistently meet or exceed customer expectations. Learn a lean approach by seeing how Kanban made a difference in four real-world situations. You'll explore how different teams used Kanban to make paradigm-changing improvements in software development. These teams were struggling with overwork, unclear priorities, and a lack of direction. As you discover what worked for them, you'll understand how to make significant changes in real-life situations. The Artefact has been developed as a resource to understand, evaluate, and use Agile and Hybrid Agile approaches. This practice guide will help you understand when, where, and how to apply Agile approaches and provides practical tools for practitioners and organizations wanting to increase agility. What you will learn Explore and learn how to build Organizational Resilience and Enterprise Maturity Model. Step-by-step solutions to implement Portfolio Kanban and Upstream Kanban. Deep dive into Agile SHIFT framework and Hybrid Agile framework. Exciting case studies and practical demonstrations on Agile SCRUM & KANBAN. Expert-ready guidance on overcoming common Agile project management misconceptions. Who this book is for This book is appealing to decision makers, product owners, project team members who can make use of this guide in improvising the productivity and efficient management of business operations without much of hassle. Table of Contents 1. Key success factors for adopting Agile SCRUM Kanban in any organization 2. Lessons learnt and pragmatic approach – Agile Scrum Kanban 3. Tricky real-world Agile SCRUM & KANBAN case studies, demos and tools 4. Agile SCRUM KANBAN Maturity assessment Nuts & Bolts 5. Useful tips & techniques for successful Agile transformation in any organization and the art of Agile development 6. Use of Agile for students and parents 7. Common Agile SCRUM KANBAN misconceptions 8. Key takeaways 9. Interview questions and answers on Agile SCRUM KANBAN 10. Glossary 11. Quiz session 12. Test your knowledge About the Authors Sudipta Malakar is an accomplished SAP practice area head, Certified IT Sr. program manager, Agile coach – Advanced level, Harvard Business School, USA, alumnus, patent holder, and an International bestselling author & speaker with more than 17 years of experience in directing SAP DEV teams in supporting many major Global fortune 500 clients in multiple large accounts. He is a certified sr. program manager (MSP practitioner), a sr. project manager (PRINCE2 Practitioner), PMP®, CSP®, ITIL(F), a certified Agile Leader(CDL), CLMM, CMM, and an advanced certified Scrum Master (A-CSM) ®, CSPO®, CSM®, KMP2, KMP1, ICP-ACC®, TKP®, ISO 9001 Lead Auditor, Lean Six Sigma Master Black Belt, CMMi (Expert). He worked in various IT companies like IBM, Wipro, Satyam, Tech Mahindra, Patni, and Syntel, and he played a crucial sr. management/Agile coach role for various global clients like Sterlite, Lufthansa, Nestle, PMI, Suncor, IPA, Canadian Pacific railways, Sony, Volvo, Allstate, and BOC Linde. LinkedIn Profile: https://www.linkedin.com/in/sudipta-malakar-csp-klmm-cdl-kmm-cspo-kmp-a-csm-icp-acc-tkp-3a794213a/
A practical guide that will help you understand the Statistical Foundations of any Machine Learning Problem. Key Features Develop a Conceptual and Mathematical understanding of Statistics Get an overview of Statistical Applications in Python Learn how to perform Hypothesis testing in Statistics Understand why Statistics is important in Machine Learning Learn how to process data in Python Description This book talks about Statistical concepts in detail, with its applications in Python. The book starts with an introduction to Statistics and moves on to cover some basic Descriptive Statistics concepts such as mean, median, mode, etc. You will then explore the concept of Probability and look at different types of Probability Distributions. Next, you will look at parameter estimations for the unknown parameters present in the population and look at Random Variables in detail, which are used to save the results of an experiment in Statistics. You will then explore one of the most important fields in Statistics - Hypothesis Testing, and then explore various types of tests used to check our hypothesis. The last part of our book will focus on how you can process data using Python, some elements of Non-parametric statistics, and finally, some introduction to Machine Learning. What you will learn Understand the basics of Statistics Get to know more about Descriptive Statistics Understand and learn advanced Statistics techniques Learn how to apply Statistical concepts in Python Understand important Python packages for Statistics and Machine Learning Who this book is for This book is for anyone who wants to understand Statistics and its use in Machine Learning. This book will help you understand the Mathematics behind the Statistical concepts and the applications using the Python language. Having a working knowledge of the Python language is a prerequisite. Table of Contents 1. Introduction to Statistics 2. Descriptive Statistics 3. Probability 4. Random Variables 5. Parameter Estimations 6. Hypothesis Testing 7. Analysis of Variance 8. Regression 9. Non Parametric Statistics 10. Data Analysis using Python 11. Introduction to Machine Learning About the Authors Himanshu Singh is an AI Technology Lead at Legato Healthcare (An Anthem Inc. Company). He has around 7 years of experience in the domain of Machine Learning and Artificial Intelligence. Himanshu is an author of three books in Machine Learning and is a trainer by passion. He is a guest faculty at various institutes like Narsee Monjee Institute of Management Studies, IMT, Vignana Jyothi Institute of Management. LinkedIn Profile: https://www.linkedin.com/in/himanshu-singh-2264a350/ Blog links: https://medium.com/@himanshuit3036 Facebook Profile: https://www.facebook.com/silli23
Doors are never locked for smart software and smart devices that are trained by smart people Key Features A book for everyone interested to know more about WSN, AI, and IoT Discover various Open source tools & techniques for research and development in these felids An easy-to-understand guide that will help you get familiar with the upcoming developments in WSN, AI, and IoT Description Almost every industry is looking for solutions for the best performance in the work that they produce. Researchers and developers are developing promising solutions that address the industrial problems to increase the effectiveness and efficiency of either the product or the service. This paradigm has changed the way many solutions and services are designed. Wireless Sensor Networks (WSN) are the backbone implementation for the Internet of Things (IoT) to be realized. For the IoT to produce efficient results, Artificial Intelligence (AI) becomes the key assistance, however, it needs careful modeling. The content for the book is planned and prepared in such a way that you will be able to understand the concept and can interpret it for their use. The concepts, technologies, processes that are discussed in the book are contemporary and futuristic. Every chapter is well planned to be a subsequent chapter for the previous. In the Summary section of each chapter, there are a few review questions and a case for research. What will you learn Learn about the most popular AI & IoT research topics Discover a few WSN, IoT and AI Simulators Get to know more about the fusion of Blockchain and IoT technologies Know more about the AI and IoT predictions in the global scenario
Learn how to work towards making the most out of a career in emerging tech Key Features Understand the core concepts related to careers in emerging tech. Learn innovative, exclusive, and exciting ways to design a successful career in ET. Reduce your learning curve by examining the career trajectories of eminent ET professionals. Ways to evolve and adapt to changing ET paradigms. Practical perspective from the field. Description Cracking the emerging tech code will help you attain your Emerging Technology (ET) career goals faster without spending years in committing avoidable mistakes, recovering from them, and learning things the hard way. You can apply practical tips in areas such as improving your ability to craft market-friendly use cases and evolving a solution approach in new and diverse tech or business environments, to propel forward your career in strategic and proactive ways. It outlines ways in which you can explore and capitalize on hidden opportunities while working on important career aspects. The anecdotes and solutions provided will aid you in getting an inside out view to reduce your learning curve. This book will help you in gaining both magnitude and direction in your ET career journey and prevent you from getting overwhelmed or pinned down by the forces of ET. Authored by an ET professional, this book will take you through a series of steps to deepen your understanding of the forces that shape one’s ET career and successfully dealing with them. It also helps bust myths, addresses fallacies, and common misconceptions that could harm one’s career prospects. There are also practical and easy-to-adopt tips, methods, tracking mechanisms, and information that will improve career standing and professional growth. This book makes it easy for you to enhance your employability and job market relevance so that you can sprint towards a rewarding career. What will you learn Through this book, you will connect with ways and means to build a strong and rewarding emerging tech career. You will be able to work on identifying the right technology and employer, enhancing employability and differentiation in the job market, addressing challenges and connecting with enablers, accurate growth strategies and execution principles. Who this book is for This book is for current and aspiring emerging tech professionals, students, and anyone who wishes to understand ways to have a fulfilling career in emerging technologies such as AI, blockchain, cybersecurity, IoT, space tech, and more. Table of Contents 1. Introduction 2. The best ET for me and some myth bursting 3. Getting prepared and charting a roadmap 4. Identifying the requirements and getting help 5. Dealing with headwinds and drawing a career change action plan 6. Building an ET friendly résumé and finding the right employer 7. Getting hired through social media 8. Job search 9. Impressing the emerging tech jury 10. The secret sauce 11. Becoming a thought leader 12. Measuring success and making course corrections 13. Drawing the two-year plan 14. Building your leadership capabilities 15. To start-up or not? 16. Communications skills: getting it right 17. Building a personal brand 18. Post-script About the Authors Prayukth has been actively involved in productizing and promoting cross eco-system collaboration in the IoT space for over half-a-decade. In recent years, he has focused on exposing APT groups, global footprint, and in evaluating the evolving threat landscape surrounding IoT and OT environments. In his current role, he has taken Subex’s IoT business to new geographies. Your Linkedin profile: https://www.linkedin.com/in/prayukthkv/