1Training OverviewThis course aims to enable participants to master the application of Python in the field of scientific research through comprehensive and systematic learning, especially how to leverage artificial intelligence technologies to promote research progress. 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. The course particularly emphasizes the practical applications of artificial intelligence in scientific writing and data processing, helping students efficiently complete their research tasks.The course includes detailed theoretical explanations and rich hands-on practices, allowing students to deeply understand and master the principles and application methods of various artificial intelligence algorithms.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 research-related projects, 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 large language model ChatGPT in scientific research, comprehensively enhancing students’ research capabilities and innovation levels. By taking this course, students will not only be able to independently complete various data analysis and model building tasks in SCI papers but also effectively apply artificial intelligence technologies in the research process to improve research efficiency and quality of results.02Training Advantages1. Attend one training session, and you can participate in relevant on-site and live courses for free for life, with no limit on the number of times, until you learn it; 2. Start learning from the most basic operations and concepts, gradually improving, open for registration regardless of prior knowledge;3. Interpretation and analysis of actual SCI papers and practical artificial intelligence application projects, detailing how Python artificial intelligence algorithms are applied to SCI paper writing and practical 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. There will be dedicated classroom hands-on practice every day to 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 people, please register quickly, and the first 30 registrants can obtain training videos and materials from previous sessions.03Training Outcomes1. Master the basics of Python programming:Through systematic learning and practice, master basic Python syntax, data structures, control flow, functions, and modules, laying a solid foundation for subsequent artificial intelligence applications;2. Familiarize yourself with scientific data analysis tools:Learn to use Numpy for scientific computing, master Matplotlib’s plotting techniques, and be able to effectively analyze and visualize scientific data to provide data support for research projects;3. Understand artificial intelligence algorithms:Deeply understand 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 actual 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. SCI paper writing and optimization skills:By detailed interpretation of classic SCI papers, master the norms and writing techniques of scientific research, learn how to use 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 FormatNovember 19, 2024 — November 21, 2024Beijing on-site/Tencent Meeting platform for 3 days of teaching
- Attend one training session, and you can participate in relevant on-site and live courses for free for life, with no limit on the number of times, until you learn it;
- Get electronic handouts and models in advance after registration and payment, allowing for early preparation;
- 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 practical experts in the field of artificial intelligence, with 12 years of experience in AI project development and 10 years of experience in AI industry training.Preferring a teaching style that combines theory and practice, the course arrangement is clear and complete, progressing from shallow to deep.Has presided over several national and corporate major projects, holds 20 patents, has published 3 books related to artificial intelligence, and has completed multiple AI-related projects for schools, hospitals, enterprises, meteorological bureaus, and other units.Invited to provide internal training on artificial intelligence technology for many institutions, including China Mobile, China Telecom, Bank of China, Huaxia Bank, Pacific Insurance, State Grid, CNOOC, Gree Electric, and many other Fortune 500 companies.Top IT training platform with a 99% satisfaction rate from 300,000 students;05
Training Outline
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Major Chapters |
Minor Chapters |
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Chapter 1: Common Artificial Intelligence Project Application Case Analysis |
1. Security patrol system based on camera2. Cloud type recognition3. User comment sentiment classification4. Thyroid CT image classification5. Industrial defect detection6. Automobile component installation detection |
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Chapter 2:Python Artificial Intelligence 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:Introduction to Python Environment |
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 practice) Python environment installation configuration3. (Hands-on practice) Using print4. (Hands-on practice) Operators and variables5. (Hands-on practice) Loops6. (Hands-on practice) Lists, tuples, and dictionaries7. (Hands-on practice) If conditions8. (Hands-on practice) Functions9. (Hands-on practice) Modules10. (Hands-on practice) Using classes11. (Hands-on practice) File reading and writing12. (Hands-on practice) Exception handling |
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Chapter 5: Learning Numpy Scientific Computing Module |
1. (Hands-on practice) Attributes of numpy2. (Hands-on practice) Creating array3. (Hands-on practice) Operations of numpy4. (Hands-on practice) Random number generation and matrix operations5. (Hands-on practice) Indexing in numpy |
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Chapter 6: Learning Matplotlib Plotting Toolkit |
1. (Hands-on practice) Basic usage2. (Hands-on practice) Figure3. (Hands-on practice) Setting axes4. (Hands-on practice) Legend5. (Hands-on practice) Scatter plot |
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Chapter 8: Common Machine Learning Algorithms (Students complete in class practice) |
1. (Hands-on practice) Introduction and use of linear regression algorithm2. (Hands-on practice) Introduction and use of Lasso regression algorithm3. (Hands-on practice) Introduction and use of KNN algorithm4. (Hands-on practice) Introduction and use of SVM algorithm5. (Hands-on practice) Introduction and use of K-means algorithm6. (Hands-on practice) Introduction and use of XGBoost algorithm7. (Hands-on practice) Introduction and use of LightGBM algorithm8. (Hands-on practice) Summary and analysis of all machine learning algorithm usage techniques9. (Hands-on practice) Train machine learning algorithms with 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. Processing numerical feature types4. Processing 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 practice) |
1. Interpretation of related paper content and analysis of how the project applies 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 check 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 value. Select appropriate features for model training4. Exploratory data analysis – Use Seaborn’s pairplot to plot the relationships between different features – Draw heatmaps to analyze the correlations between features5. Model building and training – Model selection: Choose multiple classification algorithms (e.g., K-nearest neighbors, logistic regression, neural networks, decision trees, random forests, etc.) for comparison6. Model evaluation and optimization – Result visualization: Use bar charts to display the performance comparison of different models – Model interpretation: Use SHAP values to explain the model’s predictions to understand which features have the greatest impact on the model’s predictions7. Project summary – Evaluate model performance: Comprehensive evaluation of the accuracy and interpretability of the model – 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, showcasing the practical applications of machine learning algorithms2. 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 the selected deep learning algorithm is 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 practice) Plot scatter plots, line charts, bar charts, pie charts, etc., based on local data2. (Hands-on practice) Plot correlation graphs of different features3. (Hands-on practice) Plot multivariate joint distribution graphs of different data features4. (Hands-on practice) Plot visualizations of missing data5. (Hands-on practice) Plot comparison graphs of results from different model algorithms6. (Hands-on practice) Plot ROC curves of model algorithms7. (Hands-on practice) Plot feature importance ranking graphs8. (Hands-on practice) Various AI automatic plotting methods for different images |
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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 BP algorithm4. Gradient vanishing problem5. Introduction to various activation functions6. (Hands-on practice) BP algorithm to solve handwritten digit recognition problem |
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Chapter 14: Model Algorithm Optimization Methods |
1. (Hands-on practice) Explanation of Mnist dataset and softmax2. (Hands-on practice) Recognizing images using BP neural networks3. (Hands-on practice) Explanation and use of cross-entropy4. (Hands-on practice) Underfitting/correct fitting/overfitting5. (Hands-on practice) Various optimizers6. (Hands-on practice) Model saving and loading methods |
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Chapter 15: Deep Learning Algorithms – Convolutional Neural Network (CNN) Applications |
1. Introduction to CNN2. Introduction to local receptive fields and weight sharing in convolution3. Specific calculation methods of convolution4. Introduction to pooling layers (mean pooling, max pooling)5. Introduction to LeNET-5 convolutional network6. (Hands-on practice) CNN handwritten digit recognition case |
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Chapter 16: Deep Learning Algorithms – Long Short-Term Memory (LSTM) Applications |
1. Introduction to Recurrent Neural Networks (RNN)2. Specific calculation analysis of RNN3. Introduction to Long Short-Term Memory (LSTM)4. Specific calculation analysis of input gate, forget gate, and output gate5. Introduction to stacked LSTM6. Introduction to bidirectional LSTM7. (Hands-on practice) Using LSTM for gene sequence energy prediction |
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Chapter 17: Deep Learning Image Recognition Projects Based on Transfer Learning (Students complete in class practice) |
1. Detailed explanation of VGG16 model2. Detailed explanation of ResNet model3. Detailed explanation of ConvNeXt model4. (Hands-on practice) Download trained 1000-class image recognition model5. (Hands-on practice) Use trained image recognition model for various image classifications6. (Hands-on practice) Train meteorological image classification model using transfer learning7. (Hands-on practice) 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, showcasing the practical applications of deep learning algorithms2. 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 the selected deep learning algorithm is 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 Faster-RCNN Series Models |
1. Introduction to object detection projects2. Detailed explanation of R-CNN model3. Detailed explanation of SPPNET model4. Detailed explanation of Fast-RCNN model5. Detailed explanation of Faster-RCNN model |
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Chapter 20: Introduction and Application of YOLO Algorithm |
1. Structure and workflow of YOLOv12. Explanation of YOLOv1 cost function and analysis of its shortcomings3. Explanation of YOLOv2 network structure Darknet-194. YOLOv2 precision optimization – high resolution and anchor5. YOLOv2 precision optimization – dimension 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 (Students complete in class practice) |
1. Introduction to YOLOv10 detection model2. (Hands-on practice) Install YOLOv10 model3. (Hands-on practice) Self-label the images to be detected4. (Hands-on practice) Modify the model’s configuration file5. (Hands-on practice) Train the YOLOv10 object detection model6. (Hands-on practice) Use the trained YOLOv10 for image prediction |
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Chapter 22: Latest Object Segmentation Algorithm YOLOv10 Application (Students complete in class practice) |
1. Introduction to YOLOv10 segmentation model2. (Hands-on practice) Install YOLOv10 model3. (Hands-on practice) Self-label the images to be segmented4. (Hands-on practice) Modify the model’s configuration file5. (Hands-on practice) Train the YOLOv10 image segmentation model6. (Hands-on practice) 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, showcasing the practical applications of image detection and segmentation algorithms2. 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 the selected deep learning algorithm is 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 Transformer model2. Self-Attention3. Multi-Head Attention4. Introduction to Bert model5. MLM and NSP model tasks6. Using 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 – GPT4o2. Introduction to domestic large language models such as Wenxin Yiyan, Tongyi Qianwen, Kimi, Zhipu Qingyan, Xinghuo Cognition3. ChatGPT assists in paper searching and reading4. ChatGPT as your paper writing assistant5. ChatGPT assists in optimizing scientific research papers6. Become a programming expert without knowing how to code |
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Auxiliary Courses |
1. Course summary and outlook on technological development.2. Establish a Q&A group for answering questions, providing post-class support.3. Provide AIGC/GPT/AI drawing/artificial intelligence, machine learning, and deep learning textbooks, gradually improving skills after class. |
06Fee Standards
There are three categories of training fees, please choose flexibly according to your needs.
Category A:3980 yuan/person (including training fee, material fee, Category A certificate fee, guidance fee, invoice fee, etc.)
Certificate:Course completion certificate issued by Zhongke Ruanyan (Beijing) Science and Technology Center;
Category B: 4980 yuan/person (including training fee, material fee, Category A + B certificate fee, guidance fee, invoice fee, etc.)
Certificate:Senior “Large Model Application Development Engineer” professional technical talent vocational skill certificate issued by an institution under the Ministry of Education, included in the committee database, nationally recognized and verifiable;
Category C: 5980 yuan/person (including training fee, material fee, Category A + B + C category certificate fee, guidance fee, invoice fee, etc.)
Certificate:Advanced “Artificial Intelligence Application Engineer” vocational skill 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, verifiable on the official website.
07Discount Policies
1. Students can enjoy a discount of 300 yuan with a student ID;
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 (inclusive) registering, an additional free spot will be given;
5. 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 notice and confirm by phone.
