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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 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