Business Data Analysis Tutorial

Business Data Analysis Tutorial Resource Introduction:

Course Catalog

├──{10}--Unit 10 Electronic Recommendation System

| ├──{1}--Basics of Recommendation System

| ├──{2}--Recommendation system structure

| ├──{3}--Recommendations based on demographics, recommendations based on content

| ├──{4}--Recommendation algorithm based on collaborative filtering

| ├──{5}--Graph-based model, PageRank-based recommendation, association rule-based recommendation

| ├──{6}--Other recommended methods

| ├──{7}--Evaluation method of recommendation results

| ├──{8}--Evaluation indicators for recommendation results

| └──{9}--Common Problems of Recommendation System

├──{11}--Unit 11 Deep Learning

| ├──{10}--Stock prediction based on LSTM

| ├──{11}--Image positioning and recognition 1

| ├──{12}--Image positioning and recognition 2

| ├──{13}--Reinforcement Learning

| ├──{14}--Generative Adversarial Networks

| ├──{15}--Transfer Learning

| ├──{16}--Dual Learning

| ├──{17}--Review of Deep Learning

| ├──{1}--Basic concept of convolution

| ├──{2}--LeNet framework (1)

| ├──{3}--LeNet framework (2)

| ├──{4}--Convolution basic unit

| ├──{5}--Convolutional Neural Network Training

| ├──{6}--Stock prediction based on convolution

| ├──{7}--Recurrent Neural Network (RNN) Basics

| ├──{8}--Recurrent Neural Network Training and Examples

| └──{9}--Long Short-Term Memory Network LSTM

├──{12}--Unit 12: Practical Machine Learning Course Discussion

| └──{1}--Course teaching method discussion

├──{1}--Unit 1 Introduction to Machine Learning

| ├──{1}--Introduction to Machine Learning

| ├──{2}--Machine Learning Process

| ├──{3}--Common Machine Learning Algorithms (1)

| ├──{4}--Common Machine Learning Algorithms (2)

| ├──{5}--Common Problems in Machine Learning

| ├──{6}--Preparation for machine learning

| └──{7}--Common application areas of machine learning

├──{2}--Unit 2 classification algorithm

| ├──{10}--Bayesian network model algorithm

| ├──{11}--Application of Bayesian Network

| ├──{12}--Principal component analysis and singular value decomposition

| ├──{13}--Discriminant analysis

| ├──{1}--Decision Tree Overview

| ├──{2}--ID3 algorithm

| ├──{3}--C4.5 algorithm and CART algorithm

| ├──{4}--Discretization of continuous attributes and overfitting problems

| ├──{5}--Ensemble learning

| ├──{6}--Basic concepts of support vector machines

| ├──{7}--Principle of Support Vector Machine

| ├──{8}--Application of Support Vector Machine

| └──{9}--Naive Bayes model

├──{3}--Unit 3: Basics of Neural Networks

| ├──{1}--Introduction to Neural Networks

| ├──{2}--Neural network related concepts

| ├──{3}--BP neural network algorithm (1)

| ├──{4}--BP neural network algorithm (2)

| └──{5}--Application of Neural Networks

├──{4}--Unit 4 Cluster Analysis

| ├──{1}--The concept of cluster analysis

| ├──{2}--Metrics for cluster analysis

| ├──{3}--Partition-based method (1)

| ├──{4}--Partition-based method (2)

| ├──{5}--Density-based clustering and hierarchical clustering

| ├──{6}--Model-based clustering

| └──{7}--EM algorithm

├──{5}--Unit 5 Visual Analysis

| ├──{1}--Basics of Visual Analysis

| ├──{2}--Visual analysis method

| └──{3}--Data analysis case of online teaching

├──{6}--Unit 6 Correlation Analysis

| ├──{1}--Basic concepts of association analysis

| ├──{2}--Apriori algorithm

| └──{3}--Application of association rules

├──{7}--Unit 7 Regression Analysis

| ├──{1}--Basics of regression analysis

| ├──{2}--Linear regression analysis

| └──{3}--Nonlinear regression analysis

├──{8}--Unit 8 Text Analysis

| ├──{1}--Introduction to Text Analysis

| ├──{2}--Basic concepts of text analysis

| ├──{3}--Language model, vector space model

| ├──{4}--Morphology, word segmentation, and syntactic analysis

| ├──{5}--Semantic analysis

| ├──{6}--Text analysis application

| ├──{7}--Introduction to Knowledge Graph

| ├──{8}--Knowledge Graph Technology

| └──{9}--Knowledge graph construction and application

└──{9}--Unit 9 Distributed Machine Learning, Genetic Algorithms

| ├──{1}--Basics of Distributed Machine Learning

| ├──{2}--Distributed Machine Learning Framework

| ├──{3}--Parallel decision tree

| ├──{4}--Parallel k-means algorithm

| ├──{5}--Parallel multiple linear regression model

| ├──{6}--Genetic Algorithm Basics

| ├──{7}--Genetic Algorithm Process

| ├──{8}--Application of Genetic Algorithm

| └──{9}--Bee Swarm Algorithm