Location:Training / Machine Learning/Algorithm / Python machine learning applications / Linear regression + linear fitting of the relationship between house prices and house size

This course is aimed at all types of programming learners. It explains the currently popular machine learning-related technologies and methods, helps learners master the basic ability of machine learning algorithms to solve general problems using Python language, and gets a glimpse of the mysteries of cutting-edge machine learning algorithms.
This course introduces scikit-learn, a popular machine learning algorithm library in the Python computing ecosystem. These algorithms have extremely wide application potential in engineering, information, management, economics and other disciplines, and are used by major scientific research institutes and internationally renowned institutions around the world. Widely used by companies, it includes two parts: compulsory content and elective content.

The compulsory contents include:
(1) Understanding machine learning, introducing classic algorithms by introducing the basic problems of machine learning (classification, clustering, regression, dimensionality reduction);
(2) Python third-party library sklearn (scikit-learn), explaining the application of machines Learn algorithms to quickly solve real-world problems.
The elective content includes:
(1) Explanation of the machine learning principles behind AlphaGo (reinforcement learning);
(2) Demonstration of game battle examples to demonstrate the powerful charm of independent learning through examples.

According to the content characteristics of the third-party library, the course is divided into 6 content modules and 2 practical modules:

Module 1: Basic ideas and principles of machine learning vs. sklearn library
Module 2: Clustering, algorithms and use cases of unsupervised learning (sklearn in K-means, DBSCAN)
Module 3: Dimensionality reduction, algorithms and use cases
of unsupervised learning (PCA, NMF in sklearn) Module 4: Classification, algorithms and use cases of supervised learning (KNN, Naive Bayes, Decision Tree in sklearn )
Module 5: Regression, algorithms and use cases of supervised learning (linear regression, non-linear review in sklearn)
Module 6 (Practical): Writing examples of supervised learning to achieve handwriting recognition, algorithm comparison and analysis
Module 7 (Elective): Reinforcement learning methods, Deep Learning
Module 8 (Elective, Practical Combat): Practical Project: Flappy Bird Game Intelligent Battle