Data Science from Scratch with Python: Step-by-Step Beginner Guide for Statistics, Machine Learning, Deep learning and NLP using Python, Numpy, Pandas, Scipy, Matplotlib, Sciki-Learn, TensorFlow

***** BUY NOW (will soon return to 15.77 $) ***** 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 Examples The 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 Users Beginners 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 science What’s Inside This Book? Part 1: Data Science Fundamentals, Concepts and Algorithms IntroductionStatisticsProbabilityBayes’ 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 Selection Part 2: Data Science in Practice Overview 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 Processing Frequently Asked Questions Q: Does this book include everything I need to become a data science expert? A: Unfortunately, no.