Python machine learning applications

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

• Chapter 1：Machine learning course content guide (6 minutes and 46 seconds)

• Chapter 2：Installation of Sklearn library (4 minutes and 43 seconds)

• Chapter 3：Sklearn library standard data sets and basic functions (7 minutes and 29 seconds)

• Chapter 4：Unsupervised learning course guide (9 minutes and 33 seconds)

• Chapter 5：Clustering K-Means+31 Provincial and Municipal Household Consumption Survey (8 minutes and 12 seconds)

• Chapter 6：Clustering Dbscan+ Student Monthly Internet Time Distribution Clustering Example (9 minutes and 17 seconds)

• Chapter 7：NMF for dimensionality reduction (7 minutes and 16 seconds)

• Chapter 8：PCA for dimensionality reduction (6 minutes and 37 seconds)

• Chapter 9：Example: Clustering-based whole-image segmentation (5 minutes and 17 seconds)

• Chapter 10：This week’s course guide (8 minutes and 5 seconds)

• Chapter 11：Example Analysis of "Human Movement Status Information Rating" (6 minutes and 26 seconds)

• Chapter 12：KNN+Nbayes+decision tree (11 minutes and 14 seconds)

• Chapter 13："Human Movement Status Information Rating" Example Writing and Comparison (7 minutes and 48 seconds)

• Chapter 14：Examples of forecasting the rise and fall of the Shanghai Composite Index (6 minutes and 3 seconds)

• Chapter 15：Linear regression + linear fitting of the relationship between house prices and house size (8 minutes and 54 seconds)

• Chapter 16：Polynomial regression + nonlinear fitting of housing prices and house size (7 minutes and 0 seconds)

• Chapter 17：ridge regression (7 minutes and 46 seconds)

• Chapter 18："Handwriting recognition" example introduction (4 minutes and 55 seconds)

• Chapter 19：Neural network implements "handwriting recognition" example writing (10 minutes and 29 seconds)

• Chapter 20：KNN implements "handwriting recognition" example writing (6 minutes and 33 seconds)

• Chapter 21：Reinforcement Learning Basics (10 minutes and 38 seconds)

• Chapter 22：Example: Independent learning of Flappy Bird game (2 minutes and 34 seconds)

• Chapter 23：Basic framework of Flappy Bird self-learning program (10 minutes and 8 seconds)

• Chapter 24：Introduction and installation of related libraries (2 minutes and 57 seconds)

• Chapter 25：Project actual combat (13 minutes and 13 seconds)

• Chapter 26：Training results display (1 minutes and 44 seconds)

• Chapter 27：Course Summary (6 minutes and 15 seconds)