Data Analysis From Scratch With Python: Beginner Guide using Python, Pandas, NumPy, Scikit-Learn, IPython, TensorFlow and Matplotlib

***** BUY NOW (Will soon return to 25.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of becoming a data analyst using Python? If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you. 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 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 This book is a practical introduction to data science tools in Python. It is ideal for analyst's beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images. What's Inside This Book? Introduction Why Choose Python for Data Science & Machine Learning Prerequisites & Reminders Python Quick Review Overview & Objectives A Quick Example Getting & Processing Data Data Visualization Supervised & Unsupervised Learning Regression Simple Linear Regression Multiple Linear Regression Decision Tree Random Forest Classification Logistic Regression K-Nearest Neighbors Decision Tree Classification Random Forest Classification Clustering Goals & Uses of Clustering K-Means Clustering Anomaly Detection Association Rule Learning Explanation Apriori Reinforcement Learning What is Reinforcement Learning Comparison with Supervised & Unsupervised Learning Applying Reinforcement Learning Neural Networks An Idea of How the Brain Works Potential & Constraints Here's an Example Natural Language Processing Analyzing Words & Sentiments Using NLTK Model Selection & Improving Performance Sources & References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: if you want to smash Python for data analysis, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. 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 analysis and further learning will be required beyond this book to master all aspects. 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. AI Sciences Company offers you a free eBooks at http: //(http://aisciences.net/free/) aisciences.net/free/ \*\*\*\*\* BUY NOW (Will soon return to 25.59) \*\*\*\*\*\*Free eBook for customers who purchase the print book from Amazon\*\*\*\*\*\* Are you thinking of becoming a data analyst using Python? If you are looking for a complete guide to data analysis using Python language and its library that will help you to become an effective data scientist, this book is for you. 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 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 This book is a practical introduction to data science tools in Python. It is ideal for analyst's beginners to Python and for Python programmers new to data science and computer science. Instead of tough math formulas, this book contains several graphs and images. What's Inside This Book?IntroductionWhy Choose Python for Data Science & Machine LearningPrerequisites & RemindersPython Quick ReviewOverview & ObjectivesA Quick ExampleGetting & Processing DataData VisualizationSupervised & Unsupervised LearningRegressionSimple Linear RegressionMultiple Linear RegressionDecision TreeRandom ForestClassificationLogistic RegressionK-Nearest NeighborsDecision Tree ClassificationRandom Forest ClassificationClusteringGoals & Uses of ClusteringK-Means ClusteringAnomaly DetectionAssociation Rule LearningExplanationAprioriReinforcement LearningWhat is Reinforcement LearningComparison with Supervised & Unsupervised LearningApplying Reinforcement LearningNeural NetworksAn Idea of How the Brain WorksPotential & ConstraintsHere's an ExampleNatural Language ProcessingAnalyzing Words & SentimentsUsing NLTKModel Selection & Improving PerformanceSources & ReferencesFrequently Asked QuestionsQ: Is this book for me and do I need programming experience? A: if you want to smash Python for data analysis, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.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 analysis and further learning will be required beyond this book to master all aspects.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.**AI Sciences Company offers you a free eBooks at http: //[aisciences.net/free/](http://aisciences.net/free/)** Preface 4 Why the AI Sciences Books are different? 4 Step By Step Guide and Visual Illustrations and Examples 4 Who Should Read This? 4 From AI Sciences Publisher 7 Author Biography 10 Table of Contents 11 Introduction 14 2. Why Choose Python for Data Science & Machine Learning 15 Python vs R 15 Widespread Use of Python in Data Analysis 16 Clarity 16 3. Prerequisites & Reminders 17 Python & Programming Knowledge 17 Installation & Setup 17 Is Mathematical Expertise Necessary? 18 4. Python Quick Review 19 Tips for Faster Learning 24 5. Overview & Objectives 25 Data Analysis vs Data Science vs Machine Learning 25 Possibilities 26 Limitations of Data Analysis & Machine Learning 26 Accuracy & Performance 27 6. A Quick Example 28 Iris Dataset 28 Potential & Implications 29 7. Getting & Processing Data 31 CSV Files 31 Feature Selection 34 Online Data Sources 35 Internal Data Source 36 8. Data Visualization 37 Goal of Visualization 37 Importing & Using Matplotlib 38 9. Supervised & Unsupervised Learning 44 What is Supervised Learning? 44 What is Unsupervised Learning? 46 How to Approach a Problem 46 10. Regression 48 Simple Linear Regression 48 Multiple Linear Regression 51 Decision Tree 56 Random Forest 58 11. Classification 62 Logistic Regression 62 K-Nearest Neighbors 66 Decision Tree Classification 69 Random Forest Classification 73 12. Clustering 76 Goals & Uses of Clustering 76 K-Means Clustering 77 Anomaly Detection 80 13. Association Rule Learning 82 Explanation 82 Apriori 83 14. Reinforcement Learning 87 What is Reinforcement Learning? 87 Comparison with Supervised & Unsupervised Learning 88 Applying Reinforcement Learning 88 15. Artificial Neural Networks 92 An Idea of How the Brain Works 92 Potential & Constraints 93 Here’s an Example 94 16. Natural Language Processing 98 Analyzing Words & Sentiments 98 Using NLTK 99 Thank you ! 101 Sources & References 102 Software, libraries, & programming language 102 Datasets 102 Online books, tutorials, & other references 102 Thank you ! 103