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The knowledge graph is a very popular technology recently, integrating web crawling, natural language processing, machine learning, deep learning, graph databases, complex network analysis, and many other hot technologies into one. The technology density is high, making it a product that companies are very interested in, such as constructing semantic search, Q&A platforms, and intelligent interfaces, with clear and visible value, rapidly becoming a hot topic.
Learning the knowledge graph, there is currently a severe lack of reference books that focus on the current technical hotspots, with only some old books about “semantic networks” and “knowledge bases” lingering in the inventory. These books are often difficult to understand, piling up a large amount of complex concepts and jargon to obscure the specific technical connotations, and the focus of attention has long been outdated. Outsiders feel lost, the more they read, the more confused they become. This course aims to build a valuable knowledge graph through practical experience, using data, code, specific algorithms, and grounded products, fully revealing various aspects of the knowledge graph, enabling learners to quickly get started, create their own knowledge graph products, and replicate them in a series of typical scenarios. At the same time, a large number of cutting-edge research materials will be selected, allowing learners to follow a path to treasure, returning laden with knowledge.
This course is the first in a series of courses on “knowledge graph”. Subsequent courses such as “Knowledge Graph Design” and “Practical Q&A Systems Based on Knowledge Graphs” will be arranged as necessary, along with courses on applying knowledge graphs in specific industries, such as finance, healthcare, and education.
Lesson 1: Overview of Knowledge Graphs, is the knowledge graph a “new bottle for old wine”? The IT technologies, algorithms, and challenges involved, and the relationship between knowledge graphs and natural language processing, deep learning, and other mainstream fields of artificial intelligence.
Lesson 2: Building a simple “Honor of Kings” knowledge graph, web crawler, entity recognition, relation extraction, implementing semantic search and Q&A platform.
Lesson 3: Knowledge graph storage, graph databases, and query languages, and the visualization of knowledge graphs, searching on the “Honor of Kings” knowledge graph.
Lesson 4: Revisiting the enterprise knowledge graph, building a medical knowledge graph project with commercial value, mastering entity recognition techniques, thoroughly understanding LSTM and CRF, and entity recognition methodology.
Lesson 5: One of the challenges of knowledge graphs: in-depth discussion on relation extraction, based on deep learning methods.
Lesson 6: Another challenge of knowledge graphs: practical disambiguation.
Lesson 7: How are chatbots trained? Setting up a WeChat chatbot, detailing the technical composition of chatbot Q&A platforms.
The course will start on March 25, 2025, and will last approximately 10 weeks.
Those interested in applying knowledge graphs, designers with relevant technical skills, researchers, enthusiasts, and professionals from various industries looking to enhance product experiences using knowledge graphs. Learners should have some understanding in NLP, machine learning, etc. (For those unfamiliar with these fields, a 3-5 hour introductory video will be provided before the course to grasp basic NLP techniques and deep learning.) Familiarity with Python is required.
Familiarity with the relevant techniques for building knowledge graphs, ultimately enabling participants to create a commercially valuable knowledge graph.
Tigerfish, founder of the well-known database website ITPUB, and founder of the renowned data analysis website Data Gold. An expert in databases and data analysis, with extensive knowledge in IT and mathematics. He will lead his data analysis team to complete the entire course.
He Cuiyi, graduated from Zhongshan University with a major in statistics, and is a professional instructor at Data Gold.
He has opened several courses related to data analysis and mining, such as “Statistics Fundamentals for Big Data,” “Matrix Fundamentals for Big Data,” and “Financial Time Series Analysis,” and has also conducted relevant training in various companies on R language and data analysis. He has a deep understanding of data analysis, having collaborated with various companies in different fields, participating in multiple data analysis projects, such as Huawei, Guangzhou Metro, etc.
National Unified Consultation Hotline: 4008-0010-006
Course Discussion QQ: 3039177420
Consultation QQ: 22220100060, 22220100006 (Online during work hours)
Customer Service WeChat: dataguru_keful
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