Build Real World Machine Learning Project

机器学习很重要，因为它可以让企业了解客户行为和业务运营模式的趋势，并支持新产品的开发。当今许多领先的公司，例如 Facebook、Google 和 Uber，都将机器学习作为其运营的核心部分。机器学习已成为许多公司的重要竞争优势。

经典机器学习通常根据算法如何学习使其预测更准确来分类。有四种基本方法：监督学习、无监督学习、半监督学习和强化学习。数据科学家选择使用的算法类型取决于他们想要预测的数据类型。

监督学习：在这种类型的机器学习中，数据科学家为算法提供标记的训练数据，并定义他们希望算法评估相关性的变量。指定算法的输入和输出。

无监督学习：这种类型的机器学习涉及对未标记数据进行训练的算法。该算法扫描数据集以寻找任何有意义的连接。算法训练所依据的数据以及它们输出的预测或建议是预先确定的。

半监督学习：这种机器学习方法涉及前两种类型的混合。数据科学家可能会提供一种算法，主要是标记训练数据，但该模型可以自由地自行探索数据并发展自己对数据集的理解。

强化学习：数据科学家通常使用强化学习来教机器完成具有明确定义规则的多步骤过程。数据科学家对算法进行编程以完成任务，并在计算如何完成任务时给出正面或负面的提示。但在大多数情况下，算法自行决定沿途采取哪些步骤。

Instructors: The Machine Learning
26 sections • 141 lectures • 15h 47m
Video: MP4 1280×720 44 KHz | English + Sub
Updated 10/2022 | Size: 8.989 GB

Learn To Build Machine Learning Projects Practically

What you’ll learn

• Real life case studies and projects to understand how things are done in the real world
• Learn best practices when it comes to Data Science Workflow
• Learn How to improve your Machine Learning Models
• Learn best practices for real-world data sets.
• Make powerful analysis

Requirements

basic knowledge of machine learning

Description

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.

Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.

Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.

Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

Who this course is for
Beginners in machine learning