Data Science From Scratch With Python: Step-by-step Guide

***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****Are you thinking of learning data science from scratch using Python? (For Beginners)If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book “Data Analysis from Scratch with Python”, Peter Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners 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. Readers are advised to adopt a hands on approach, which would lead to better mental representations.Step by Step Guide and Visual Illustrations and ExamplesThe Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process. Target UsersBeginners who want to approach data science, but are too afraid of complex math to startNewbies in computer science techniques and data scienceProfessors, 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 scienceWhat’s Inside This Book?Part 1: Data Science Fundamentals, Concepts and AlgorithmsIntroductionStatisticsProbabilityBayes’ Theorem and Naïve Bayes AlgorithmAsking the Right QuestionData AcquisitionData PreparationData ExplorationData ModellingData PresentationSupervised Learning AlgorithmsUnsupervised Learning AlgorithmsSemi-supervised Learning AlgorithmsReinforcement Learning AlgorithmsOverfitting and UnderfittingThe Bias-Variance Trade-offFeature Extraction and SelectionPart 2: Data Science in PracticeOverview of Python Programming LanguagePython Data Science ToolsJupyter NotebookNumerical Python (Numpy)PandasScientific Python (Scipy)MatplotlibScikit-LearnK-Nearest NeighborsNaive BayesSimple and Multiple Linear RegressionLogistic RegressionGLM modelsDecision Trees and Random forestPerceptronsBackpropagationClusteringNatural Language ProcessingFrequently Asked QuestionsQ: 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 using Python and further learning will be required beyond this book to master all aspects.Q: Can I have a refund if this book doesn’t fit 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.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews"This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.."- Lei Xia, Data Scientist Expert at Facebook