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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写统计与机器学习Statistical and Machine Learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写统计与机器学习Statistical and Machine Learning方面经验极为丰富，各种代写机器学习Statistical and Machine Learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Advanced Analytics in Data Science

Data science and advanced analytics comprise more than simply statistical analysis and mathematical models. The field encompasses machine learning, forecasting, text analytics, and optimization. Data scientists must use all these techniques to solve business problems. In several business scenarios, a combination of some of these models are required to propose a feasible solution to a specific problem.

There are basically two types of machine learning models: supervised learning (when the response variable (also known as the target) is known and used in the model) and unsupervised learning (when the target is unknown or not used in the model). The input variables (also called features in the machine learning field or the independent attributes in the statistical field) contain information about the customers, who they are, how they consume the product or service, how they pay for it, for how long they are customers, from where they came from, where they went to, among many other descriptive information.

The target is the business event of interest, for example, when a customer makes churn, purchases a product, makes a payment, or simply uses a credit card or makes a phone call. This event is called a target because this is the event the model will try to predict, classify, or estimate. This is what the company wants to know. (A target is also called a label in the machine learning field or dependent attribute in the statistical field.) Unsupervised models do not require the target. These models are used to generate insights about the data, or market, or customers, to evaluate possible trends or to better understand some specific business scenarios. These models do not aim to classify, predict, or estimate a business event in the future.

As shown in Table 1.1, regression, decision tree, random forest, gradient boosting, neural network, and support vector machine are examples of supervised models. Clustering, association rules, sequence association rules, path analysis, and link analysis are examples of unsupervised models. There is a variation of these types of models called semi-supervised models. Semi-supervised models involve a small amount of data where the target is known and a large amount of data where the target is unknown. There are also models associated with reinforcement learning, where the algorithm is trained by using a system to reward the step when the model goes in the right direction and to punish the step when the model goes in the wrong direction. Semi-supervised models are becoming more prevalent and are often implemented in artificial intelligence applications. For example, reinforcement learning can be used to train a model to learn and take actions in self-driving cars. During the training, if the car drives safely in the road, the learning step is rewarded because it is going in the right direction. On the other hand, if the car drives off the road, the learning step is punished because the training is going in the wrong direction.
As statistical models try to approximate reality through mathematical formalized methods making predictions about future events, machine learning automates some of the most important steps in analytical models, the learning process. Machine learning models automatically improve the learning steps based on the input data and the objective function.

统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Data Monetization

Information is the new gold. Data science can be used for organizations to monetize the most important asset that they have, which is data. Data monetization refers to the act of generating measurable economic benefits from available data sources. Companies in different industries have very sensitive and important data about customers or people in general. Think about telecommunications companies that have precise information about where people go at any point in time. Where people are in a point in time can be easily monetized. Data scientists can use the results of mobile apps that track what customers do, when, and with who. They can also use the results of web search engines that know exactly what customers are looking for in a point in time. Some examples of data monetization include:

• Location-Based Marketing – develop specialized offers and promotions that are delivered to targeted customers via their mobile devices. For example, if you enter a specific area in a city, you might get a coupon for companies located in that area.
• Micro Segmentation – create highly detailed customer segments that can be used to send very specific campaigns, promotions, and offerings over time.
• Third-party Partnerships – partner with different companies to combine customer data to enrich the information used to create analytical models and data analyses about business actions.
• Real-Time Data Analysis – analyze real-time data streams from different types of transactions to keep customers consuming or using products and services with no outages or intermittent breaks.
In conclusion, data scientists can develop and deploy a set of techniques and algorithms to address business problems. Today, companies are dealing with information that comes in varieties and volumes never encountered before. As data scientists increase their skills in areas such as machine learning, statistical analysis, forecasting, text analytics, and optimization, their value to the company will increase over time.

统计与机器学习代考

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师