01Training OverviewThis course aims to enable students to master the application of Python in the field of scientific research through comprehensive and systematic learning, particularly on how to leverage artificial intelligence technologies to advance scientific research. The course content covers everything from basic Python programming to advanced machine learning and deep learning algorithms, gradually guiding students to master key skills such as scientific data analysis, model design and training, and scientific plotting. Additionally, the course emphasizes the practical applications of artificial intelligence in scientific writing and data processing, helping students efficiently complete research tasks.The course features detailed theoretical explanations and rich hands-on exercises in class, allowing students to deeply understand and master the principles and application methods of various artificial intelligence algorithms.The course content includes learning scientific computing and plotting tools such as Numpy and Matplotlib, the application and optimization of machine learning algorithms, and the practical applications of deep learning algorithms in image recognition and object detection. Special case analysis sessions are set up to introduce various real-world projects related to scientific research, helping students apply the knowledge learned to specific research projects. Furthermore, the course introduces the latest artificial intelligence technologies, such as the YOLOv10 object detection and segmentation algorithm and the application of large language models like ChatGPT in research, comprehensively enhancing students’ research capabilities and innovation levels. Through this course, students will not only be able to independently complete various data analysis and model construction tasks in SCI papers but also effectively apply artificial intelligence technologies in the research process to improve research efficiency and result quality.02Training Advantages1. Attend one training session, and you can participate in related on-site and live courses for free for life, unlimited times, until you master the material;2. Start learning from the most basic operations and concepts, gradually advancing, open to those with or without a foundation;3. Analyze and interpret actual SCI papers and practical artificial intelligence application projects, detailing how Python artificial intelligence algorithms can be applied to SCI paper writing and real project applications;4. The course content includes a large number of practical case operations, deeply analyzing the best applications of Python artificial intelligence algorithms in academic research and project applications;5. Daily dedicated practical exercises in class ensure students master the details of practical operations;6.Establish a course group, providing permanent Q&A services.Complete course video playback will be provided after the course ends;7. This course is limited to 50 participants, register quickly; the first 30 registrants will receive past training videos and materials.03Training Outcomes1. Master the basics of Python programming:Through systematic learning and practice, master the basic syntax, data structures, control flow, functions, and modules of Python programming, laying a solid foundation for subsequent artificial intelligence applications;2. Familiarize with scientific data analysis tools:Learn to use Numpy for scientific computing, master Matplotlib’s plotting techniques, and effectively analyze and visualize scientific data to provide data support for research projects;3. Understand artificial intelligence algorithms:Deeply understand the core concepts and commonly used algorithms of machine learning and deep learning, such as linear regression, KNN, SVM, CNN, LSTM, etc., and be able to apply these algorithms in research projects for data modeling and predictive analysis;4. Apply artificial intelligence to solve research problems:Through practical case studies, master the entire process of data preprocessing, feature engineering, model building, and optimization, enhancing the ability to solve practical research problems;5. Master the latest artificial intelligence technologies:Learn and apply the latest object detection and segmentation algorithms such as YOLOv10 to improve the efficiency and quality of research work;6. Skills in writing and optimizing SCI papers:Through detailed interpretations of classic SCI papers, master the norms and writing techniques of scientific research, learn how to utilize artificial intelligence technologies for data analysis, model training, and result presentation, enhancing the writing level and publication success rate of research papers.04Time and Training FormatJanuary 3, 2025 — January 5, 2025On-site in Beijing / Teaching on Tencent Meeting platform for 3 days
- Attend one training session, and you can participate in related on-site and live courses for free for life, unlimited times, until you master the material;
- After registering and paying, you will receive electronic handouts and models in advance for pre-study;
- Establish a course group, providing permanent Q&A services,Complete course video playback will be provided after the course ends.
04Featured ExpertsSenior experts from research institutions such as the Chinese Academy of Sciences and Tsinghua University, frontline practitioners in the field of artificial intelligence, with 12 years of experience in artificial intelligence project development and 10 years of experience in training in the artificial intelligence industry.They prefer a teaching style that combines theory and practice, with a clear and complete course structure that progresses from simple to complex.They have hosted several major national and corporate projects, hold 20 patents, and have published three books related to artificial intelligence, having completed multiple artificial intelligence-related projects for schools, hospitals, enterprises, meteorological bureaus, etc.They have been invited to conduct internal training in artificial intelligence technology for many institutions, including China Mobile, China Telecom, Bank of China, Huaxia Bank, Pacific Insurance, State Grid, CNOOC, Gree Electric Appliances, and other companies, including several Fortune 500 companies.Top IT training platforms with a 99% satisfaction rate from 300,000 students;05
Training Outline
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Main Chapters |
Sub-Chapters |
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Chapter 1: Common Artificial Intelligence Project Application Case Analysis |
1. Security patrol system based on cameras2. Cloud type recognition3. User comment sentiment classification4. Thyroid CT image classification5. Industrial defect detection6. Automotive component installation inspection |
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Chapter 2:Python Applications in Scientific Research |
1. Application of artificial intelligence in scientific writing2. Application of artificial intelligence in scientific translation3. Application of artificial intelligence in scientific data analysis4. Application of artificial intelligence in scientific plotting5. Application of artificial intelligence in scientific model design and training6. Various application scenarios of artificial intelligence technology |
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Chapter 3:Python Environment Introduction |
1. Python integrated environment – Anaconda installation2. Python development environment – PyCharm introduction3. Python development environment – Jupyter configuration4. Basic use of Jupyter |
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Chapter 4: Basic Learning of Python |
1. Application scenarios of Python2. (Hands-on exercise) Python environment installation and configuration3. (Hands-on exercise) Using print4. (Hands-on exercise) Operators and variables5. (Hands-on exercise) Loops6. (Hands-on exercise) Lists, tuples, dictionaries7. (Hands-on exercise) If conditions8. (Hands-on exercise) Functions9. (Hands-on exercise) Modules10. (Hands-on exercise) Class usage11. (Hands-on exercise) File reading and writing12. (Hands-on exercise) Exception handling |
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Chapter 5: Learning the Numpy Scientific Computing Module |
1. (Hands-on exercise) Numpy attributes2. (Hands-on exercise) Creating arrays3. (Hands-on exercise) Numpy operations4. (Hands-on exercise) Random number generation and matrix operations5. (Hands-on exercise) Numpy indexing |
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Chapter 6: Learning the Matplotlib Plotting Toolkit |
1. (Hands-on exercise) Basic usage2. (Hands-on exercise) Figure images3. (Hands-on exercise) Setting axes4. (Hands-on exercise) Legends5. (Hands-on exercise) Scatter plots |
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Chapter 8: Common Machine Learning Algorithms (Completed by students in hands-on exercises) |
1. (Hands-on exercise) Introduction and use of linear regression algorithm2. (Hands-on exercise) Introduction and use of Lasso regression algorithm3. (Hands-on exercise) Introduction and use of KNN algorithm4. (Hands-on exercise) Introduction and use of SVM algorithm5. (Hands-on exercise) Introduction and use of K-means algorithm6. (Hands-on exercise) Introduction and use of XGBoost algorithm7. (Hands-on exercise) Introduction and use of LightGBM algorithm8. (Hands-on exercise) Summary and analysis of all machine learning algorithm usage techniques9. (Hands-on exercise) Train machine learning algorithms using your own data |
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Chapter 9: Data Feature Engineering in Machine Learning |
1. Significance of feature engineering2. Methods for filling missing values3. Handling numeric type features4. Handling multi-value ordered and unordered features5. Feature selection methods6. Data standardization and normalization processing |
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Chapter 10: Application of Machine Learning Cases in Projects (Hands-on exercises completed by students) |
1. Interpretation of related paper content, and analysis of how the project can be applied to paper writing2. Project introduction – Goal definition: Develop a machine learning model for data prediction.3. Data preprocessing – Data loading: Load the dataset and initially view the data structure and basic statistics – Data cleaning: Identify and handle outliers and missing values in the dataset. Use appropriate methods to fill missing values (e.g., mean filling) – Feature engineering: Analyze the relationship between each feature and the label values. Select appropriate features for model training4. Exploratory data analysis – Use Seaborn’s pairplot to plot the relationships between different features – Draw a heatmap to analyze the correlation between features5. Model building and training – Model selection: Choose multiple classification algorithms (such as K-nearest neighbors, logistic regression, neural networks, decision trees, random forests, etc.) for comparison6. Model evaluation and optimization – Result visualization: Use bar charts to show performance comparisons of different models – Model interpretation: Use SHAP values to explain the model’s prediction results to understand which features have the most impact on the model’s predictions7. Project summary – Evaluate model performance: Comprehensive evaluation of the model’s accuracy and interpretability – Discussion and improvement: Discuss possible improvement methods and potential challenges in practical applications based on model performance |
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Chapter 11: Application of Machine Learning Algorithms in SCI Papers |
1. Detailed interpretation of several classic SCI papers, demonstrating the practical application of machine learning algorithms2. Detailed interpretation of each paper, highlighting the reasons for algorithm selection, application process, and result analysis3. Research background and problem definition: Introduce the problems solved by the paper and the research background4. Data processing and feature engineering: Discuss data preprocessing methods and feature engineering steps5. Algorithm selection and model building process: Explain why this deep learning algorithm was chosen and describe the model building process6. Model evaluation and result discussion: Evaluate model performance, discuss experimental results and their significance |
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Chapter 12: Application of AI in Data Plotting |
1. (Hands-on exercise) Plot scatter plots, line charts, bar charts, pie charts, etc. based on local data2. (Hands-on exercise) Plot correlation coefficient graphs between different features3. (Hands-on exercise) Plot multivariate joint distribution graphs for different data features4. (Hands-on exercise) Plot visualizations of missing data5. (Hands-on exercise) Plot comparison graphs of results from different model algorithms6. (Hands-on exercise) Plot ROC curve graphs for model algorithms7. (Hands-on exercise) Plot feature importance ranking graphs8. (Hands-on exercise) Other various AI automatic plotting methods |
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Chapter 13: Basics of Deep Learning Algorithms – Neural Networks |
1. Single-layer perceptron2. Activation functions, loss functions, and gradient descent3. Introduction to the BP algorithm4. Gradient vanishing problem5. Introduction to various activation functions6. (Hands-on exercise) BP algorithm to solve handwritten digit recognition problems |
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Chapter 14: Model Algorithm Optimization Methods |
1. (Hands-on exercise) Explanation of the Mnist dataset and softmax2. (Hands-on exercise) Use BP neural networks to recognize images3. (Hands-on exercise) Explanation and use of cross-entropy4. (Hands-on exercise) Underfitting/correct fitting/overfitting5. (Hands-on exercise) Various optimizers6. (Hands-on exercise) Methods for saving and loading models |
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Chapter 15: Deep Learning Algorithms – Applications of Convolutional Neural Networks (CNN) |
1. Introduction to CNN convolutional neural networks2. Introduction to local receptive fields and weight sharing in convolution.3. Specific calculation methods of convolution4. Introduction to pooling layers (average pooling, max pooling)5. Introduction to LeNET-5 convolutional networks6. (Hands-on exercise) CNN handwritten digit recognition case |
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Chapter 16: Deep Learning Algorithms – Applications of Long Short-Term Memory Networks (LSTM) |
1. Introduction to RNN recurrent neural networks2. Specific calculation analysis of RNN3. Introduction to Long Short-Term Memory Networks (LSTM)4. Specific calculation analysis of input gates, forget gates, and output gates5. Introduction to stacked LSTMs6. Introduction to bidirectional LSTMs7. (Hands-on exercise) Use LSTM for gene sequence energy prediction |
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Chapter 17: Deep Learning Image Recognition Projects Based on Transfer Learning (Completed by students in hands-on exercises) |
1. Detailed explanation of the VGG16 model2. Detailed explanation of the ResNet model3. Detailed explanation of the ConvNeXt model4. (Hands-on exercise) Download trained 1000-class image recognition models5. (Hands-on exercise) Use trained image recognition models for various image classifications6. (Hands-on exercise) Use transfer learning to train meteorological image classification models7. (Hands-on exercise) Train your own image classification dataset |
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Chapter 18: Application of Deep Learning Algorithms in SCI Papers |
1. Detailed interpretation of several classic SCI papers, demonstrating the practical application of deep learning algorithms2. Detailed interpretation of each paper, highlighting the reasons for algorithm selection, application process, and result analysis3. Research background and problem definition: Introduce the problems solved by the paper and the research background4. Data processing and feature engineering: Discuss data preprocessing methods and feature engineering steps5. Algorithm selection and model building process: Explain why this deep learning algorithm was chosen and describe the model building process6. Model evaluation and result discussion: Evaluate model performance, discuss experimental results and their significance |
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Chapter 19: Explanation of the Faster-RCNN Series Models |
1. Introduction to object detection projects2. Detailed explanation of the R-CNN model3. Detailed explanation of the SPPNET model4. Detailed explanation of the Fast-RCNN model5. Detailed explanation of the Faster-RCNN model |
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Chapter 20: Introduction and Application of the YOLO Algorithm |
1. Structure and workflow of YOLOv12. Explanation of the cost function of YOLOv1 and analysis of its shortcomings3. Explanation of the YOLOv2 network structure Darknet-194. YOLOv2 precision optimization – high resolution and anchor5. YOLOv2 precision optimization – dimensional clustering6. YOLOv2 precision optimization – direct location prediction7. YOLOv2 precision optimization – fine-grained features and multi-scale training8. Explanation of YOLOv3 structure9. Explanation of YOLOv4 algorithm10. Explanation of YOLOv5 algorithm |
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Chapter 21: Latest Object Detection Algorithm YOLOv10 Application (Completed by students in hands-on exercises) |
1. Introduction to the YOLOv10 detection model2. (Hands-on exercise) Install the YOLOv10 model3. (Hands-on exercise) Manually label the images to be detected4. (Hands-on exercise) Modify the model’s configuration file5. (Hands-on exercise) Train the YOLOv10 object detection model6. (Hands-on exercise) Use the trained YOLOv10 for image predictions |
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Chapter 22: Latest Object Segmentation Algorithm YOLOv10 Application (Completed by students in hands-on exercises) |
1. Introduction to the YOLOv10 segmentation model2. (Hands-on exercise) Install the YOLOv10 model3. (Hands-on exercise) Manually label the images to be segmented4. (Hands-on exercise) Modify the model’s configuration file5. (Hands-on exercise) Train the YOLOv10 image segmentation model6. (Hands-on exercise) Use the trained YOLOv10 for image segmentation |
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Chapter 23: Application of Image Detection and Segmentation Algorithms in SCI Papers |
1. Detailed interpretation of several classic SCI papers, demonstrating the practical application of image detection and segmentation algorithms2. Detailed interpretation of each paper, highlighting the reasons for algorithm selection, application process, and result analysis3. Research background and problem definition: Introduce the problems solved by the paper and the research background4. Data processing: Discuss data preprocessing methods5. Algorithm selection and model building process: Explain why this deep learning algorithm was chosen and describe the model building process6. Model evaluation and result discussion: Evaluate model performance, discuss experimental results and their significance |
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Chapter 24: Natural Language Processing Tasks |
1. Introduction to the Transformer model2. Self-Attention3. Multi-Head Attention4. Introduction to the Bert model5. MLM and NSP model tasks6. Using the Bert model for user comment classification |
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Chapter 25: Introduction to Large Language Model ChatGPT |
1. Introduction to OpenAI’s latest model – GPT-4o2. Introduction to domestic large language models such as Wenxin Yiyan, Tongyi Qianwen, Kimi, Zhipu Qingyan, and Xinghuo Cognition3. ChatGPT as an assistant for paper searching and reading4. ChatGPT as your paper writing assistant5. ChatGPT for optimizing scientific research papers6. Become a programming expert without knowing how to code |
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Supporting Courses |
1. Course summary and outlook on technological development.2. Establish a Q&A group for answering questions after class.3. Equipped with AIGC/GPT/AI drawing/artificial intelligence, machine learning, and deep learning textbooks, gradually improving skills after class. |
06Fee Standards
Training fees are divided into three categories, please choose flexibly according to your needs.
Category A:3980 yuan/person (including training fee, material fee,A category certificate fee, guidance fee, invoice fee, etc.)
Certificate:Course completion certificate issued by the Zhongke Soft Research (Beijing) Science and Technology Center;
Category B: 4980 yuan/person (including training fee, material fee,A category+B category certificate fee, guidance fee, invoice fee, etc.)
Certificate:Senior “Large Model Application Development Engineer” professional technical talent vocational skills certificate issued by an institution under the Ministry of Education, included in the committee database, and searchable nationwide;
Category C: 5980 yuan/person (including training fee, material fee,A category+B category+C category certificate fee, guidance fee, invoice fee, etc.)
Certificate:Advanced “Artificial Intelligence Application Engineer” vocational skills certificate issued by an institution under the Ministry of Industry and Information Technology; this certificate can serve as proof of professional technical personnel’s vocational ability assessment, as well as an important basis for the employment, appointment, grading, and promotion of professional technical personnel, and can be checked on the official website.
07Discount Policies
1. Students with student ID can get a discount of 300 yuan;
2. For 2 people registering, each can reduce 200 yuan;
3. For 3 people registering, each can reduce 400 yuan;
4. For 5 or more people (inclusive) registering, an additional free spot will be provided;
The above discount policies cannot be enjoyed simultaneously, only one can be applied.
08Registration Method
Please scan the QR code below to register online. After successful registration, we will send you a training notification and confirm by phone.
