|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-course-a-beginners-guide/
课程评论：没有评论
**课程名称：** 机器学习入门指南 **课程概述：** 本课程旨在为初学者介绍机器学习的科学，即如何训练机器自主学习。机器学习正在改变世界，颠覆全球各行各业和岗位职能。实际上，我们每天都在不知不觉中多次使用机器学习，从面部解锁手机到生物识别打卡，其应用几乎无处不在。 在当前的机器学习时代，每一位有志于成为数据科学家的专业人士都应该掌握机器学习技术和工具，并将其应用于解决现实世界的商业问题。 **机器学习的三大主要类别：** 1. **监督学习 (Supervised Machine Learning):** * 当拥有带有已知结果（即机器学习中的“标签”）的历史数据，并希望预测未来的结果时，就需要使用监督学习算法。 * **分类问题 (Classification Problems):** 用于将结果划分到不同的类别。例如，预测客户是否会违约，这是银行非常关注的分类问题。 * **回归问题 (Regression Problem):** 用于回答“多少”的问题。例如，预测客户违约的预期金额，这属于回归问题。 2. **无监督学习 (Unsupervised Machine Learning):** * 当不旨在精确预测某个特定结果，而是进行数据分割或聚类时，则使用无监督学习。 * 例如，银行希望对其客户进行细分，以了解他们的行为模式。这是一个无监督学习问题，因为我们不预测任何具体的输出。 3. **强化学习 (Reinforcement Learning):** * 被认为是实现真正人工智能的希望，其潜力巨大。 * 相较于传统机器学习，强化学习是一个稍微复杂但对未来至关重要的主题。 **学习目标：** 本课程将帮助您理解机器学习的基本概念，掌握监督学习、无监督学习和强化学习这三大核心分支，并为解决实际问题打下坚实的基础。
Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally.Machine learning is so extensive that you probably use it numerous times a day without even knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage.In this age of machine learning, every aspiring data scientist is expected to up-skill themselves in machine learning techniques & tools and apply them in real-world business problems.Machine Learning problems can be divided into 3 broad classes:Supervised Machine LearningUnsupervised Machine LearningReinforcement LearningSupervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future - you would use Supervised Machine Learning algorithms. Supervised Machine Learning problems can again be divided into 2 kinds of problems:Classification Problems: When you want to classify outcomes into different classes. For example - whether a customer would default on their loan or not is a classification problem which is of high interest to any BankRegression Problem: When you are interested in answering how much - these problems would fall under the Regression umbrella. For example - what is the expected amount of default from a customer is a Regression problemUnsupervised Machine Learning: There are times when you don't want to exactly predict an Outcome. You just want to perform a segmentation or clustering. For example - a bank would want to have a segmentation of its customers to understand their behavior. This is an Unsupervised Machine Learning problem as we are not predicting any outcomes here.Reinforcement Learning: It is said to be the hope of true artificial intelligence. And it is rightly said so because the potential that Reinforcement Learning possesses is immense. It is a slightly complex topic as compared to traditional machine learning but an equally crucial one for the future.
