Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event. Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook Description Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book. What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is for Data Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
Make NLP easy by building chatbots and models, and executing various NLP tasks to gain data-driven insights from raw text data Key FeaturesGet familiar with key natural language processing (NLP) concepts and terminologyExplore the functionalities and features of popular NLP toolsLearn how to use Python programming and third-party libraries to perform NLP tasksBook Description Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you've never done it before? With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises. The book starts with an introduction to NLP. You'll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you'll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you'll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews. By the end of this book, you'll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text. What you will learnObtain, verify, clean and transform text data into a correct format for useUse methods such as tokenization and stemming for text extractionDevelop a classifier to classify comments in Wikipedia articlesCollect data from open websites with the help of web scrapingTrain a model to detect topics in a set of documents using topic modelingDiscover techniques to represent text as word and document vectorsWho this book is for This book is for beginner to mid-level data scientists, machine learning developers, and NLP enthusiasts. A basic understanding of machine learning and NLP is required to help you grasp the topics in this workshop more quickly.
Campus Calling is the story of four aspiring doctors. Pratham is a rich brat whose brain does not allow him to store anything but he gets everything in life as if he is the favorite son of “Lady Luck.” Hrehaan is a boy whose brain can be qualified as possessing exceptional talent but physically not so much, thanks to his childhood illness. Sanjay, a typical scholar boy; physically, and emotionally. He is a hard-working guy, he works so hard that seeing his efforts, other guys stops trying too. And Mariyam is a rich and beautiful girl, a lethal combination. She is so beautiful that once you see her; you will forget to ask questions about the quality of her brain. All got enrolled in a medical college to achieve their goal via different merits and for different purpose. During their journey in the medical college, they realize that you need more than one x-factor to succeed in life. Success will depend on factors like, talent, hard work, luck and money. Campus life shows them everything including their first failure, first love, worst betrayal, true friendship and pure sacrifice.
From award-winning educator, innovation expert, and Global Teacher Prize finalist, Rohan Roberts, comes a provocative look at why our current education system is not fit for purpose and why we need to overhaul it. Cosmic Citizens and Moonshot Thinking: Education in an Age of Exponential Technologies takes a fresh approach to what we need to do differently to prepare our children for a world of exponential technologies, disruptive innovations, and ubiquitous A.I. In this groundbreaking book, Roberts outlines the purpose of education in a world of increased outsourcing and automation and explains how we can future-proof our youth to survive and thrive in a world of accelerating change. Through interactions with corporate leaders, interviews with principals, meetings with parents, and surveys of students, this book considers how the best and brightest students would overhaul their education system. The book highlights the role of neuroscience in education and explores several fascinating concepts such as radical openness, abundance mindsets, the gig economy, the technological singularity, intelligent optimism, the age of imagination, humanics, transhumanism, and the importance of Enlightenment values as we advance into the 21st Century. Underpinning this book is a constant focus on the importance of bringing a sense of awe into education and fostering a sense of cosmic wonder when contemplating human purpose and human existence. Written in a style that is discursive, contemplative, and with a sense of urgency, this book will appeal to students, parents, teachers, school principals, and to anyone who recognises that the only real and long-lasting way to create a better society is to first fix our education system.
Sucrose (table sugar) is considered as sugar by most of the people. Though sweet, sugars are the causes for many bitter experiences faced by the modern day civilized man. Sugar, directly or indirectly is considered as culprit for many diseases like diabetes, obesity, atherosclerosis, etc., which prompted the search for a suitable substitute.' At this given point of time, there is no such substitute which can replace sugar (which is versatile) in all aspects. The dental profession shares an interest in the search for safe, palatable sugar substitutes, as there is established evidence suggesting the causal relationship between sugar and dental caries. Use of sugar substitutes in preventive dentistry is gaining importance. Replacing sugar with a suitable sugar substitute to combat dental caries is an option wide open. This is a small effort to give an elaborate discussion on sugar substitutes and their role in dental health and also remove the myths about them and give a clear cut idea on them.
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