Artificial intelligence is a comprehensive major. From understanding the basics to in-depth study, there is still a lot to learn. It involves Python language, data processing and data analysis, machine learning algorithms, natural language processing NLP, computer vision CV, data mining and other technologies. Artificial intelligence needs to learn the following:
1. Python programming
Familiar with the Python language of artificial intelligence, establish programming thinking and object-oriented programming thinking, and master the advanced Python syntax necessary for artificial intelligence development.
Python basic syntax, Python data processing, functions, file reading and writing, exception handling, modules and packages, object-oriented, network programming, multitasking programming, advanced syntax, Python data structure.
2. Data processing and statistical analysis
Master SQL and Pandas to complete data analysis and visualization operations. Master the use of Linux common commands and databases.
Linux, MySQL and SQL, Numpy matrix operation library, Pandas data cleaning, Pandas data sorting, Pandas data visualization, Pandas data analysis projects.
3. Machine Learning
Master the basic principles of machine learning algorithms, proficiently use various data analysis tools for data extraction and data display, and be able to use machine learning related algorithms for predictive analysis.
Machine learning, K-nearest neighbor algorithm, linear regression, logistic regression, clustering algorithm, decision tree, ensemble learning, advanced machine learning algorithm, user portrait cases, e-commerce operation data modeling and analysis cases.
4. Data Mining Practical Projects
Use machine learning algorithms to solve the problems of classification, clustering, and regression in actual business, and complete data mining projects.
Neural network foundation, deep learning multi-framework comparison, Pytorch framework.
5. Basics of deep learning and NLP natural language processing
Master the basics of deep learning and classic neural network algorithms; master the world's popular PyTorch technology, and complete basic algorithms for natural language processing, such as RNN, LSTM, GRU and other technologies.
Introduction to NLP, text preprocessing, RNN and variants, Transformer principle, traditional sequence model, transfer learning.
6. ChatGPT technology
Use the ChatGPT model to complete the related functions of chat robots and question answering systems, and master the application of large-scale knowledge graph technology and natural language processing in multiple fields.
Introduction to ChatGPT, detailed explanation of ChatGPT principles, actual combat of ChatGPT projects, building chatbots, chatbots and question-answering systems based on large pre-trained models.
7. NLP natural language processing
Complete projects, master NLP natural language processing projects such as multi-scenario intelligent text classification or knowledge graphs and text summaries, and advance advanced artificial intelligence development.
Extractive text summarization solutions, generative text summarization solutions, autonomous training word vector solutions, decoding solution optimization solutions, data enhancement optimization solutions, training strategy optimization solutions, GPU deployment solutions, CPU deployment solutions, Massive text rapid classification baseline model solution, solution based on pre-training model optimization, model quantization optimization solution, model pruning optimization solution, model knowledge distillation optimization solution, mainstream transfer learning model fine-tuning optimization solution .
8. Computer Vision CV
Master data structures and algorithms, core machine learning, deep learning, master computer vision algorithms, such as target segmentation and classic CV network CNN, residual network, Yolo and SSD, etc.
Machine Learning Algorithms and ScikitLearn, Deep Learning Algorithms and Pytorch, Data Structure Algorithms, Multi-industry Data Mining Projects and NLP Expansion, Neural Networks, Introduction to Image and Vision Processing, Object Classification and Classical CV Networks, Object Detection and Classical CV Networks, Object Segmentation and classical CV network.
How to learn artificial intelligence
Artificial intelligence (AI) is a broad field that studies how to make computers and other machines intelligent. The degree of development of AI varies by application area. Not only natural language, artificial intelligence has achieved great development in machine learning, computer vision, language translation, text mining/classification, speech recognition, robotics, etc., but there are still many challenges to be solved. If you want to get into this field but still have a lot of doubts about studying this field. You can take a look at the latest version of 2023 launched by Dark Horse Programmers-artificial intelligence learning roadmap.