机器学习入门和深度学习，带测验

欢迎学习机器学习基础知识！你有兴趣了解机器如何学习并自己做出预测吗？如果是这样，这是一个完美的起点。机器学习是人工智能的一个分支，专注于开发能够自我学习和改进的计算机程序。其核心是，机器学习就是教会计算机识别模式，并根据数据做出决策。通过向计算机提供数据和算法，机器可以分析大量数据并做出准确的预测。最常见的机器学习类型是监督学习。有了监督学习，机器就有了标记数据和一套如何使用这些数据进行预测的指令。监督学习通常用于解决分类问题，如图像识别、面部识别和自然语言处理。另一种类型的机器学习是无监督学习。Machine Learning an introduction and deep learning with quiz

与监督学习不同，无监督学习不需要标记数据。相反，无监督学习算法在没有任何人工输入的情况下寻找数据中的模式和关系。无监督学习通常用于识别大型数据集中的模式和聚类。强化学习是第三种机器学习。通过强化学习，机器被赋予一个目标和一组奖励。然后，机器通过反复试验来学习，使用来自环境的反馈来确定最佳行动方案。这种类型的机器学习通常用于开发自主机器人和自动驾驶汽车。机器学习的应用是无穷无尽的。从面部识别到医疗诊断，机器学习正在彻底改变我们与世界互动的方式。通过了解机器学习的基础知识，你可以开始探索这项技术的惊人可能性。

由克里斯托弗·毕晓普创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子学习|语言:英语 |时长:5节课(45分钟 ) |大小:937 MB

Tensorflow和Scikit-learn的先决条件

你会学到什么
强化学习- Q学习和深度Q学习方法
在数据科学面试中，你会感到自信
你会熟悉机器学习中使用的许多基本算法。
你就会确切地知道什么是机器学习，什么不是。

要求
对机器学习概念的基本理解会有所帮助，但不是必需的。

这门课程是给谁的
任何想进入机器学习的人


A Prerequisite for Tensorflow and Scikit-learn

What you’ll learn
Reinforcement learning – Q learning and deep Q learning approaches
You will feel confident during Data Science interview
You’ll be familiar with many of the basic algorithms used in machine learning.
You’ll know exactly what machine learning is and what it isn’t.

Requirements
A basic understanding of the concepts of machine learning will be helpful but isn’t required.

Description
Welcome to the Basics of Machine Learning! Are you interested in understanding how machines can learn and make predictions on their own? If so, this is the perfect place to start. Machine Learning is a branch of artificial intelligence that focuses on the development of computer programs that are able to learn and improve on their own. At its core, Machine Learning is all about teaching computers to recognize patterns and make decisions based on data. By giving computers data and an algorithm to work with, machines can analyze large amounts of data and make accurate predictions. The most common type of Machine Learning is supervised learning. With supervised learning, a machine is given labeled data and a set of instructions on how to use the data to make predictions. Supervised learning is often used to solve classification problems such as image recognition, facial recognition, and natural language processing. Another type of Machine Learning is unsupervised learning. Unlike supervised learning, unsupervised learning does not require labeled data. Instead, unsupervised learning algorithms look for patterns and relationships in data without any human input. Unsupervised learning is often used to identify patterns and clusters in large datasets. Reinforcement learning is a third type of Machine Learning. With reinforcement learning, the machine is given a goal and a set of rewards. The machine then learns by trial and error, using feedback from its environment to determine the best course of action. This type of Machine Learning is often used to develop autonomous robots and self-driving cars. The applications of Machine Learning are endless. From facial recognition to medical diagnosis, Machine Learning is revolutionizing the way we interact with the world. By understanding the basics of Machine Learning, you can start to explore the amazing possibilities of this technology.

Who this course is for
Anyone trying to get into Machine learning