1Training Overview
With the rapid advancements in the fields of artificial intelligence, machine learning, and deep learning, the update and iteration of AI technologies have accelerated in recent years, bringing many new technologies worth learning. Various organizations and universities are increasingly seeking stronger capabilities in machine learning and deep learning to promote in-depth development of scientific research and project work. To this end, we have invited top AI experts from China to hold a training course focused on the application of Python in the field of artificial intelligence.
This course will delve into the latest technologies and powerful functions of AI, and its wide applications. Python, as a high-level programming language, has become the preferred language for machine learning and deep learning projects due to its concise and clear syntax and strong library support. In this training, we will learn through practical cases how to use Python for data analysis, model building, algorithm development, and AI application deployment.
To accommodate the learning needs of different students, the training will be conducted in a combination of online and offline formats, ensuring that each student can gain the best learning experience in a flexible learning environment. The teaching content is easy to understand, helping you quickly grasp and apply artificial intelligence technology, providing professional answers and support for problems encountered by students in practical operations, and assisting students in applying the knowledge learned based on their research fields or work needs, truly realizing technology transformation and innovation upgrade.
02Training Advantages
1. After participating in one training session, you can attend the same online and offline courses for free for life;
2. After the training, instructors will provide students with their phone numbers and emails for post-class Q&A, fully ensuring effective results after training;
3. We will collect students’ interests and content they wish to learn in advance, providing one-on-one targeted guidance during and after class;
4. Online classes will be taught using Tencent Meeting platform, ensuring training quality, with synchronous video recording available for unlimited viewing.
03Time and Training Format
December 17, 2024 — December 19, 2024
On-site in Beijing/Tencent Meeting platform for 3 days of instruction
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Using Tencent Meeting platform for instruction, with years of online training experience, ensuring training quality. Recorded videos are available for unlimited viewing;
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After registration and payment, electronic handouts and models will be provided in advance for pre-study;
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After the training, instructors will provide students with their phone numbers and emails for technical support, fully ensuring effective results after training.
04Main Instructor
Senior experts from research institutions such as the Chinese Academy of Sciences and Tsinghua University. Frontline practical experts in the field of artificial intelligence, proficient in Python AI programming technology, focusing on various open-source projects in deep learning, such as TensorFlow, Caffe, Pytorch, etc. They prefer a teaching style that combines theory and practice, with a course arrangement that progresses from simple to complex, with a clear and complete system. They hold 2 patents and have completed multiple AI-related projects for schools, hospitals, enterprises, meteorological bureaus, etc. They have been invited to conduct corporate training in artificial intelligence technology for many large companies, including Fortune 500 companies like China Mobile, China Telecom, Bank of China, Huaxia Bank, Pacific Insurance, State Grid, CNOOC, and Gree Electric. The top IT training platform in the industry has a 99% satisfaction rate from 300,000 students;
05Training Outline
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Main Chapters |
Sub-Chapters |
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1. Analysis of Common AI Application Cases |
1. Security Patrol System Based on Cameras2. Cloud Species Recognition3. User Comment Sentiment Classification4. Thyroid CT Image Classification5. Industrial Defect Detection6. Automotive Component Installation Detection |
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2. 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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3. Basic Python Learning |
1. Using Print 2. Operators and Variables 3. Loops 4. Lists, Tuples, Dictionaries 5. If Conditions 6. Functions 7. Modules 8. Using Classes 9. Input Usage 10. File Reading and Writing 11. Exception Handling |
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4. Learning to Use the Scientific Computing Package Numpy |
1. Properties of Numpy 2. Creating Arrays 3. Operations in Numpy 4. Random Number Generation and Matrix Operations 5. Indexing in Numpy 6. Array Merging |
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5. Learning the Plotting Toolkit Matplotlib |
1. Basic Usage 2. Figure Images 3. Setting Axes 4. Legends 5. Scatter Plots |
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6. Introduction to Basic Concepts of Machine Learning and Deep Learning |
1. Artificial Intelligence / Machine Learning / Neural Networks / Deep Learning2. Introduction to Training Set / Validation Set / Test Set3. Supervised Learning / Unsupervised Learning / Self-supervised Learning4. Classification Applications / Regression Applications / Clustering Applications5. Various Common Applications of Artificial Intelligence6. How AI Algorithms are Trained7. Introduction to Common Architectures in Deep Learning |
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7. Regression Algorithms |
1. Univariate Linear Regression 2. Cost Function 3. Gradient Descent Method 4. Sklearn Univariate Linear Regression Application5. Multivariate Linear Regression 6. Sklearn Multivariate Linear Regression Application7. Non-linear Regression Application 8. Case Study: Relationship Between Wine Quality and Time9. Case Study: Relationship Between Transportation Frequency, Distance, and Time |
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8. Machine Learning Algorithms |
1. Introduction to Regression Algorithms 2. Introduction to KNN Algorithm 3. Introduction to Decision Tree Algorithm 4. Introduction to Support Vector Machine Algorithm5. Introduction to K-means Algorithm |
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9. Data Feature Engineering in Machine Learning |
1. Significance of Feature Engineering2. Methods for Filling Missing Values3. Processing Numeric Features4. Handling Multi-value Ordered and Unordered Features5. Feature Selection Methods6. Data Standardization and Normalization Processing |
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10. Commonly Used Machine Learning Algorithms |
1. Introduction and Use of Linear Regression Algorithm 2. Introduction and Use of Lasso Regression Algorithm3. Introduction and Use of KNN Algorithm 4. Introduction and Use of SVM Algorithm5. Introduction and Use of K-means Algorithm 6. Introduction and Use of XGBoost Algorithm7. Introduction and Use of LightGBM Algorithm8. Summary and Analysis of All Machine Learning Algorithm Usage Tips9. Training Machine Learning Algorithms with Your Own Data |
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11. User Default Prediction Feature Engineering Case Study |
1. Filling Missing Values2. Handling Unique Values3. Feature Selection4. Handling Multi-value Ordered and Unordered Features5. Processing Numeric Features6. Data Standardization Processing7. Comparing Dimensionality Reduction with and without PCA8. Model Training9. Model Evaluation |
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12. Basics of Deep Learning Algorithms – Neural Networks |
1. History of Artificial Neural Networks2. Single-layer Perceptron3. Activation Functions, Loss Functions, and Gradient Descent Method4. Introduction to BP Algorithm5. Gradient Vanishing Problem6. Introduction to Various Activation FunctionsCase Study: BP Algorithm Solving Handwritten Digit Recognition Problem |
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13. Basic Applications of Tensorflow |
1. Tensorflow Linear Regression2. Tensorflow Non-linear Regression3. Explanation of Mnist Data Set and Softmax4. Building Handwritten Digit Recognition with BP Neural Network5. Explanation and Use of Cross-Entropy6. Overfitting, Regularization, Dropout7. Various Optimizers8. Model Saving and Loading Methods |
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14. Application of Convolutional Neural Networks (CNN) |
1. CNN Convolutional Neural Networks2. Introduction to Local Receptive Fields and Weight Sharing in Convolution.3. Specific Calculation Methods for Convolution4. Introduction to Pooling Layers (Average Pooling, Max Pooling)5. Introduction to Same Padding and Valid Padding6. Introduction to LeNET-5 Convolutional Network7. CNN Handwritten Digit Recognition Case Study |
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15. Application of Long Short-Term Memory Networks (LSTM) |
1. Introduction to Recurrent Neural Networks (RNN)2. Specific Calculation Analysis of RNN3. Introduction to Long Short-Term Memory Networks (LSTM)4. Specific Calculation Analysis of Input Gate, Forget Gate, and Output Gate5. Introduction to Stacked LSTM6. Introduction to Bidirectional LSTM7. LSTM Application Case Study |
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16. Introduction to Common Image Recognition Models |
1. AlexNet2. VGG163. Inceptionv1-v44. ResNet5. Inception-ResNet6. EfficientNet-v1-v2 |
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17. Using Trained EfficientNet for Various Image Predictions |
1. Download Trained 1000 Classification Image Recognition Model2. Use Trained Image Recognition Model for Image Classification |
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18. Transfer Learning – Training Your Own Weather Phenomenon Classification Model with EfficientNet |
1. Data Preparation2. Data Augmentation3. Model Building4. Model Training5. Result Testing |
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19. Introduction to Transformer Architecture |
1. Introduction to Transformer Model2. Self-Attention3. Multi-Head Attention |
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20. Bert Model |
1. Introduction to Bert Model2. MLM and NSP Model Tasks3. Case Study: Using Bert Model for User Comment Classification |
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21. Deep Learning Image Recognition Project Based on Transfer Learning |
1. Detailed Explanation of VGG16 Model2. Detailed Explanation of ResNet Model3. Detailed Explanation of ConvNeXt Model4. Download Trained 1000 Classification Image Recognition Model5. Use Trained Image Recognition Model for Various Image Classifications6. Train Your Own Image Classification Dataset |
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22. Explanation of Faster-RCNN Series Models |
1. Introduction to Object Detection Project2. Detailed Explanation of R-CNN Model3. Detailed Explanation of SPPNET Model4. Detailed Explanation of Fast-RCNN Model5. Detailed Explanation of Faster-RCNN Model6. Implementing Faster-RCNN Object Detection with Tensorflow |
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23. Introduction and Application of YOLO Algorithm |
1. Structure and Workflow of YOLOv12. Explanation of YOLOv1 Cost Function and Analysis of Its Disadvantages3. Explanation of YOLOv2 Network Structure Darknet-194. YOLOv2 Precision Optimization – High Resolution and Anchor5. YOLOv2 Precision Optimization – Dimensional Clustering6. YOLOv2 Precision Optimization – Direct Position 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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24. Latest Object Detection Algorithm YOLOv10 Application |
1. Introduction to YOLOv10 Detection Model2. Installing YOLOv10 Model3. Manually Labeling Images to be Detected4. Modifying the Model’s Configuration File5. Training YOLOv10 Object Detection Model6. Using the Trained YOLOv10 for Image Prediction7. Implementing Helmet Detection Project |
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25. Introduction to OCR Models |
1. Introduction to OCR Algorithms2. Using OCR Algorithms to Recognize ID Card Photos3. Using OCR Algorithms to Recognize Rotated Photos4. Using OCR Algorithms to Recognize Nucleic Acid Test Screenshots and Collect Relevant Information to Save to Files |
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26. Natural Language Processing Tasks |
1. Introduction to Transformer Models2. Self-Attention3. Multi-Head Attention4. Introduction to Bert Models5. MLM and NSP Model Tasks6. Using Bert Model for User Comment Classification |
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27. Face Recognition Technology |
1. Face Recognition Technology, Detection Examples, Verification Examples, Recognition Examples2. Face Attributes (Age, Gender, Ethnicity, Expression) Recognition |
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Q&A and Learning Platform |
1. Post-class projects, Q&A, sharing of learning materials, one-on-one Q&A2. Free technical guidance after the training, free participation in subsequent training;3. One-on-one guidance to solve content of interest and learning for students, with online and offline effects being the same, teachers will remotely solve your problems one-on-one4. Equipped with materials and Python textbooks for gradual improvement after class5. Providing the latest Python installation program and remote installation service6. Content of interest and learning or self-topic content (lesson preparation explanation) |
06Fee Standards
There are three types of training fees, please choose flexibly according to your needs.
Type A:3980 RMB/person (including training fee, material fee, Type A certificate fee, guidance fee, invoice fee, etc.)
Certificate:National Science and Technology Research “Python Application Engineer” (Senior) Professional Competency Certificate, this certificate can serve as an important basis for evaluating the competency of technical personnel in relevant units, salary increases for employees, promotions, assessments, and appointments.
Type B: 4980 RMB/person (including training fee, material fee, Type A + Type B certificate fee, guidance fee, invoice fee, etc.)
Certificate:Senior Vocational Technical Certificate “Python Technology Application Engineer” issued by the Ministry of Industry and Information Technology of the People’s Republic of China, can be used for professional training, title evaluation, tax deduction, talent hiring, job seeking, education assessment, etc., listed in the national vocational qualification directory.
Type C: 5880 RMB/person (including training fee, material fee, Type A + B + C certificate fee, guidance fee, invoice fee, etc.)
Certificate:Senior Vocational Skills Certificate “Artificial Intelligence Application Engineer” issued by the China Communication Industry Association NTC; can be used for professional training, title evaluation points, talent hiring, job seeking, education assessment, etc., issued by a national first-level academic society.
07Discount Policy
1. Students can get a discount of 300 RMB with their student ID;
2. For 2 people registering, each can reduce 200 RMB;
3. For 3 people registering, each can reduce 400 RMB;
3. For 5 or more people (inclusive) registering, an additional free spot will be given;
4. The above discount policies cannot be enjoyed simultaneously, only one can be chosen.
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.
