机器学习--线性回归1

机器学习-结论 (Machine Learning - Conclusion)

This tutorial has introduced you to Machine Learning. Now, you know that Machine Learning is a technique of training machines to perform the activities a human brain can do, albeit bit faster and better than an average human-being. Today we have seen that the machines can beat human champions in games such as Chess, AlphaGO, which are considered very complex. You have seen that machines can be trained to perform human activities in several areas and can aid humans in living better lives.

本教程向您介绍了机器学习。 现在，您知道机器学习是一种训练机器以执行人脑可以执行的活动的技术，尽管它比普通人更快，更好。 今天，我们已经看到，这些机器可以在国际象棋，AlphaGO等游戏中击败人类冠军，这些游戏被认为非常复杂。 您已经看到，可以训练机器在多个区域执行人类活动，并且可以帮助人类过上更好的生活。

Machine Learning can be a Supervised or Unsupervised. If you have lesser amount of data and clearly labelled data for training, opt for Supervised Learning. Unsupervised Learning would generally give better performance and results for large data sets. If you have a huge data set easily available, go for deep learning techniques. You also have learned Reinforcement Learning and Deep Reinforcement Learning. You now know what Neural Networks are, their applications and limitations.

机器学习可以是有监督的也可以是无监督的。 如果您的数据量较少且需要明确标记的数据用于培训，请选择“监督学习”。 对于大数据集，无监督学习通常可以提供更好的性能和结果。 如果您拥有容易获得的庞大数据集，请使用深度学习技术。 您还已经学习了强化学习和深度强化学习。 您现在知道了神经网络是什么，它们的应用和局限性。

Finally, when it comes to the development of machine learning models of your own, you looked at the choices of various development languages, IDEs and Platforms. Next thing that you need to do is start learning and practicing each machine learning technique. The subject is vast, it means that there is width, but if you consider the depth, each topic can be learned in a few hours. Each topic is independent of each other. You need to take into consideration one topic at a time, learn it, practice it and implement the algorithm/s in it using a language choice of yours. This is the best way to start studying Machine Learning. Practicing one topic at a time, very soon you would acquire the width that is eventually required of a Machine Learning expert.

最后，在开发自己的机器学习模型时，您研究了各种开发语言，IDE和平台的选择。 接下来需要做的是开始学习和练习每种机器学习技术。 主题很广泛，这意味着宽度很大，但是如果您考虑深度，则可以在几个小时内学习每个主题。 每个主题彼此独立。 您需要一次考虑一个主题，对其进行学习，实践并使用您选择的语言在其中实现算法。 这是开始学习机器学习的最佳方法。 一次练习一个主题，很快您将获得机器学习专家最终所需的宽度。

Good Luck!

祝好运！

翻译自: https://www.tutorialspoint.com/machine_learning/machine_learning_conclusion.htm

机器学习--线性回归1