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Learn By Examples - Asian Eastern Recipes [Singapore Food Tour and Learn to Cook Singapore Food]
Have you ever want to tour Singapore and cook Singapore food? If yes, this book is for you. This book aims to be a Singapore Food Tour Guide and Cookbook. This book will guide you from Changi Airport to Bedok Hawker Centre to Orchard Road to Istana Park to Marina Bay Sands to Merlion to Boat Quay to Clarke Quay. This book will guide you to find good food in Singapore. When you go back to your country, this book will teach you how to cook Singapore Food. Contents 1. Singapore Food Tour (95 pages out of 325 pages.) 2. Chinese Food (Fried Rice, Oyster Egg, Steam Fish, Ginger Onion Garlic Stir Fry Chicken,) 3. Malay Food (Sambal Belacan Stir Fry Mussel, Cuttlefish, Razor Calms, Chicken) 4. Indian Food (Indian Curry Chicken) 5. Western Food (Cheese Cake, Chocolate Chips Cookies, Bread, Soy Baked Chicken, ) 6. Korean Food (Kim Chi, Kim Chi Fried Rice) 7. Japanese Food (Japanese Beancurd, Beancurd Kumbu Miso Soup, Udon Soup, ) 8. Thai Food (Tom Kha Gai, Green Curry Paste, Green Curry, Tom Yum Soup) 9. Grow Your Own Food(Grow Beansprouts, Grow Microgreens)
Learn By Examples: A Quick Guide to Data Mining with RapidMiner and Weka
This book aim to equip the reader with RaidMiner and Weka and Data Mining basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) using Weka and RapidMiner. Content Covered: - Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) - Getting Started (INstall Weka and RapidMiner) - Prediction and Classification (Prediction and Classification) - Machine Learning Basics (Kmeans Clustering, Decision Tree, Naive Bayes, KNN...
A Quick Guide to Data Mining with Weka and Java using Weka
This technical book aim to equip the reader with Weka, Data Mining in a fast and practical way. There will be many examples and explanations that are straight to the point. Contents 1. Introduction (What is data science, what is data mining, CRISP DM Model, what is text mining, three types of analytics, big data) 2. Getting Started (INstall Weka) 3. Prediction and Classification (Prediction and Classification) 4. Machine Learning Basics (KMeans Clustering, Decision Tree, Naive Bayes, KNN, Neural Network) 5. Data Mining with Weka (Data Understanding using Weka, Data Preparation using Weka, Model Building and Evaluation using Weka) 6. Java interact Weka (Use Java to use Weka, in order to develop your own prediction or classification system) 7. Conclusion This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/machine-learning-with-java-and-weka/?couponCode=SPECIALCOUPON
A Practical Guide to Forecasting Financial Market Volatility
Financial market volatility forecasting is one of today's most important areas of expertise for professionals and academics in investment, option pricing, and financial market regulation. While many books address financial market modelling, no single book is devoted primarily to the exploration of volatility forecasting and the practical use of forecasting models. A Practical Guide to Forecasting Financial Market Volatility provides practical guidance on this vital topic through an in-depth examination of a range of popular forecasting models. Details are provided on proven techniques for building volatility models, with guide-lines for actually using them in forecasting applications.
Learn By Examples - A Quick Guide To Data Science With Python
This book aim to equip the reader with Python Programming and Data Science basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) and deployment using Python. Content Covered: IntroductionGetting Started (Installing WinPython, IDE, ...)Language Essentials (variables, list, data types manipulations, ...)Language Essentials II (conditional statements, loops, ...)Object Essentials (Modules, Class and Objects, ...)Data Mining with Python (Pandas, ScikitLearn, ...) We will be using opensource tools and IDE, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into python programming, with a touch on data mining. This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/fundamentals-of-python-for-data-mining/?couponCode=EBOOKSPECIAL ISBN: 978-163535299-3
Learn By Examples - A Quick Guide to Java Programming for Text Mining and NLP
This book aim to equip the reader with Java Programming, Text Mining and Natural Language Processing basics. There will be many examples and explanations that are lucid and straight to the point. You will be walked through various projects and develop your own text mining application. This book will show you how to use Stanford NLP libraries also. Asides, we have also uploaded some of our own softwares at: http://DSTK.Tech Content Covered: IntroductionGetting Started (Installing IDE, ...)Language Essentials I (variables, data types, ...)Language Essentials II (loops, if... else..., methods)Object Essentials (classes, inheritance, polymorphism, encapsulation, ...)Text Mining Essentials (Import Text Files, Text Transformation (lowercase, stopwords), Text Understanding (Stanford NLP), Text Classification (Stanford Classifier) )ISBN: 978-1-63535-546-8
Learn R for Applied Statistics
Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statisti...
A Common Treasure
Wisconsin's Swiss community of New Glarus is widely known today because of an enterprising brewery located there. Almost lost in that success story is the background saga of the community's beginning - a Wisconsin immigration story like no other. Now New Glarus native Duane H. Freitag reconstructs the dramatic first ten years of what was then a colony of eastern Switzerland's Canton of Glarus. The demanding labor, the heartbreak, and the achievements of that era are told with pathos and pride. The settlement, created to provide a common home and secure economic base for those who felt compelled to leave their alpine homeland, put down strong roots in those first ten years. It still flourishes today and its ties to the Old World remain strong. For the historian, this volume provides a comprehensive chronological account and mentions all of the early Swiss immigrants who built up the settlement, how they arrived in Wisconsin, and their impact on the community and the state. For the pleasure reader, the pioneer life of these Swiss immigrants unfolds in surprising ways.
Volatility
Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.
