Introduction to Data Science: Essential Concepts

By Peters Morgan

******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science with easiest way (For Beginners)? If you are looking for a complete introduction to data science, this book is for you. After his great success with his first book "Data Analysis from Scratch with Python," Peters Morgan publish this book focusing now in data science and machine learning. Practitioners consider it as the easiest guide ever written in this domain.

From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations.

Step By Step Guide and Visual Illustrations and Examples This book is an introduction to the main concepts of data science explained with easiest examples. Peters Morgan focus on the practical aspects of using data science and machine learning algorithms, rather than the math behind them.

Target Users Target Users The book is designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach data science, but are too afraid of complex math to startNewbies in computer science techniques and data scienceProfessionals in data science and social sciencesProfessors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest wayStudents and academicians, especially those focusing on data science

What's Inside This Book? IntroductionStatisticsProbabilityBayes' Theorem and Na ve Bayes AlgorithmAsking the Right QuestionData AcquisitionData PreparationData ExplorationData ModellingData PresentationSupervised Learning AlgorithmsUnsupervised Learning AlgorithmsSemi-supervised Learning AlgorithmsReinforcement Learning AlgorithmsOverfitting and UnderfittingCorrectnessThe Bias-Variance Trade-offFeature Extraction and SelectionK-Nearest NeighborsNaive BayesSimple and Multiple Linear RegressionLogistic RegressionGLM modelsDecision Trees and Random forestPerceptronsBackpropagationClusteringNatural Language Processing Frequently Asked Questions

Q: Is this book for me and do I need programming experience? A: No programming experience is required. This book is an introduction to data science without any type of programming.

Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning and further learning will be required beyond this book to master all aspects.

Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days.

Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aisciences.net.

• Format: Paperback
• Language: English
• ISBN: 1722799579
• ISBN13: 9781722799571
• Release Date: January 1
• Publisher: Createspace Independent Publishing Platform
• Length: 118 Pages
• Weight: 0.37 lbs.
• Dimensions: 0.3" x 6.0" x 9.0"