Supervised learning. If you are hearing or reading this term for the first time, then it may be completely unclear what it means. Don't worry. In this lab, you will get a comprehensive understanding of supervised learning; and, in the next chapter of the experiment, you will learn to use supervised learning to complete data prediction.

During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the following lessons, you will be exposed to: logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods. The course will start with the principle of each of these methods. You are supposed to fully understand the implementat

In this course, you will fully understand unsupervised learning and learn to use unsupervised learning to perform data clustering.

In this course, you will learn the basic concepts and syntax of TensorFlow 2, and how to use TensorFlow 2 to implement deep learning algorithms.

In this course, you will learn the basic concepts of deep learning, including the basic principles of neural networks, the basic principles of TensorFlow, Keras and PyTorch, and the basic principles of linear regression, logistic regression, and multi-layer neural networks. You will also learn how to use TensorFlow, Keras and PyTorch to build a linear regression model, a logistic regression model, and a multi-layer neural network model.

In this course, you will learn the basics of OpenCV. You will learn how to read, write, and display images and videos. You will also learn how to draw different shapes on images and videos.

In this course, You will practice more labs of Machine Learning. This will help you to master the skills more deeply.