"Data Mining Thinking and Practice" explores the hidden value of data and builds a high-paying knowledge framework

Training course content:
This course starts from the perspective of building data mining thinking and explains data mining in detail. It is divided into three parts:
Part 1: Preparation of basic knowledge. You will build a global understanding of data mining, learn some basic knowledge that may be involved in the course (including the necessary Python language knowledge and how to build a Python environment), consolidate the basic knowledge, and help you get into the state quickly.

Part 2: Data mining process. It is better to teach a man to fish than to give him a fish. The focus of this section is to enable you to be fully prepared both mentally and behaviorally, and to fully and carefully understand the implementation process of the data mining method. From theory to practice, understand how the term data mining becomes more specific step by step.
Part III: Detailed explanation of the algorithm. It involves four major problems of data mining: classification, clustering, regression, and association analysis, as well as some knowledge of natural language processing. It focuses on introducing the concepts, advantages and disadvantages, and application scenarios of core algorithms, so that you can quickly get started with the application. The last lesson of each module will help you master practical processing skills through practical cases.
Easter egg: At the end of the column, open source tools and learning resources for data mining will be provided: If you can’t write code, you can also use these tools to perform data mining first; if you want to have a deeper learning and understanding of data mining, then these resources will also help you.
"Data Mining Thinking and Practice" explores the hidden value of data and builds a high-paying knowledge framework. Course content directory:
Opening words Master data mining and catch the epoch-making digital train Module 1: Basic knowledge of data mining
01 What problem does data mining solve?
02 Python data structure and basic syntax
03 If you want to do your work well, you must first sharpen your tools. Extension package and Python environment module 2: Data mining workflow
04 Understanding the business and data: What plans do we need to make?
05 Data preparation: How to process complete and clean data?
06 Data Modeling: How do I choose an algorithm that suits my needs?
07 Model evaluation: How to confirm that our model has met the standards?
08 Model application: Can our model solve business needs?
Module 3: Classification Problems
09 KNN algorithm: People get redder when they are around red people, and people get blacker when they are around black people
10 Decision Trees: Dating Decisions Goddesses Use
11 Naive Bayes: Calculate whether you should buy delay insurance
12 Support Vector Machine (SVM): Separating red beans from green beans with a line
13 Artificial Neural Networks: The Hottest Foundation of Deep Learning
14 Practice 1: Using XGB to implement hotel information disambiguation Module 4: Clustering problem
15 k-means clustering: Capture the leader first, find the center point, and all the nearby points are of the same type.
16 DBScan clustering: breaking the shape limitation and using density clustering
17 Practice 2: How to use word2vec and k-means clustering to find similar cities Module 5: Regression Problem
18 Linear regression and logistic regression: Finding a function to fit the data
19 Practice 3: Using Linear Regression to Predict Housing Prices Module 6: Correlation Analysis
20 Apriori and FP-Growth: The story of beer and diapers has to be told again
21 Practice 4: Use association analysis to find the relationship between attractions and gameplay Module 7: Natural language processing
22 TF-IDF: A simple, old, but useful keyword extraction technique
23 word2vec: Allowing words to perform logical operations, woman + crown = queen
24 Practice 5: Using fastText to classify news texts How to become an advanced data mining engineer Conclusion Cultivate data mining thinking and learn throughout life