Practical Training in AI Python Machine Learning and Deep Learning

Training Overview

Python has become one of the most popular programming languages: according to the latest TIOBE rankings, Python has surpassed C# and is now among the top four most popular languages globally, alongside Java, C, and C++. The simplicity, readability, and extensibility of Python, along with its numerous extension libraries, create a development environment that is well-suited for engineers and researchers to handle experimental data, create charts, and even develop scientific computing applications. Currently, companies like Microsoft, Tencent, Google, Facebook, Baidu, and Alibaba focus on deep learning as a key area of research for future industrial and internet development. Research institutions and universities such as the Chinese Academy of Sciences, Tsinghua University, and Peking University have established specialized research centers and laboratories to transform scientific and technological achievements in deep learning, significantly promoting the development of deep learning applications.

To further promote the research and development of Python, artificial intelligence, machine learning, and deep learning applications in higher education institutions, research institutes, and enterprises, the China Management Science Research Institute’s Professional Qualification Certification Training Center has invited frontline experts in the field of artificial intelligence to jointly hold a national training course on AI Python machine learning and deep learning. The course emphasizes a combination of theory and practice, focusing on hands-on operations; the content is primarily code-based, with theoretical explanations as the foundation and formula derivations as supplementary. Organized by the China Management Science Research Institute’s Professional Qualification Certification Training Center and co-organized by Zhongke Ruanyan (Beijing) Science and Technology Center (http://www.fzby.org.cn/) and Beijing Fuzhuo Baiyang Technology Co., Ltd.

Training Advantages

1. Obtain electronic handouts and data in advance after registration and payment, allowing for pre-study;

2. After attending one training session, participants can attend the same online and offline courses for free for life;

3. After the training, instructors will provide students with their phone numbers and emails for post-training Q&A, ensuring effective outcomes;

4. Online classes will be conducted using the Tencent Meeting platform to ensure training quality, with synchronous video recording available for unlimited viewing;

5. Free trial classes are available; payment is required only after satisfaction with the trial. Please register at the end of the article, and we will arrange the trial details for you;

6. Customized internal training can be arranged, where instructors come to your organization to provide training based on specific topics, projects, and technical content of interest. Please register at the end of the article, and we will coordinate the details with you;

7. Limited to 40 participants, registration is quick; the first 20 registrants will receive recordings and materials from previous training sessions.

Schedule and Training Format

September 8, 2023 — September 10, 2023

On-site in Shanghai / Tencent Meeting platform for 3 days

Classes will be conducted using the Tencent Meeting platform, with years of online training experience ensuring quality. Recorded videos will be available for unlimited viewing;
Participants will receive electronic handouts and models in advance after registration and payment for pre-study;
After the training, instructors will provide students with their phone numbers and emails for technical support, ensuring effective outcomes post-training.

Featured Experts

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 techniques, focusing on various open-source projects in deep learning, such as TensorFlow, Caffe, Pytorch, etc. They favor a teaching style that combines theory and practice, with a clear and complete course structure that progresses from basic to advanced. They hold 2 patents and have completed numerous AI-related projects for various organizations, including schools, hospitals, enterprises, and meteorological agencies. Invited to conduct internal training on AI technology for many large enterprises, including Fortune 500 companies like China Mobile, China Telecom, China Bank, Huaxia Bank, Pacific Insurance, State Grid, CNOOC, and Gree Electric. The top IT training platform in the industry has a 99% satisfaction rate among 300,000 students;

Training Outline

Day	Main Topics	Subtopics
Day 1	1. Analysis of Common AI Application Cases	1. Security Patrol System Based on Cameras 2. Cloud Type Recognition 3. User Comment Sentiment Classification 4. Thyroid CT Image Classification 5. Industrial Defect Detection 6. Automotive Component Installation Inspection
2. Introduction to Python Environment	1. Anaconda Installation 2. Jupyter Configuration 3. Basic Usage of Jupyter
3. Basic Python Learning	1. Using Print 2. Operators and Variables 3. Loops 4. Lists, Tuples, and Dictionaries 5. If Conditions 6. Functions 7. Modules 8. Class Usage 9. Input Usage 10. File Reading and Writing 11. Exception Handling 12. Data Analysis and Mining Using Python
4. Learning to Use the Scientific Computing Package Numpy	1. Numpy Attributes 2. Creating Arrays 3. Numpy Operations 4. Random Number Generation and Matrix Operations 5. Numpy Indexing 6. Array Merging
5. Learning the Plotting Package Matplotlib	1. Basic Usage 2. Figure Images 3. Setting Axes 4. Legends 5. Scatter Plots
6. Basics of AI and Machine Learning	1. Overview of Artificial Intelligence 2. Overview of Machine Learning 3. Training Set, Validation Set, Test Set 4. Supervised and Unsupervised Learning 5. Classification, Regression, Clustering 6. Analysis of Machine Learning Algorithms
7. Introduction to 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 Algorithm 5. Introduction to K-means Algorithm
Day 2	8. Feature Engineering Case Study for User Default Prediction	1. Filling Missing Values 2. Handling Unique Values 3. Feature Selection 4. Handling Ordered and Unordered Multi-Value Features 5. Handling Numeric Features 6. Data Standardization 7. Comparison of PCA Dimensionality Reduction and Non-PCA Dimensionality Reduction 8. Model Training 9. Model Evaluation
9. Basics of Deep Learning – Introduction to Neural Networks	1. History of Artificial Neural Networks 2. Single-Layer Perceptron 3. Activation Functions, Loss Functions, and Gradient Descent 4. Introduction to BP Algorithm 5. Vanishing Gradient Problem 6. Introduction to Various Activation Functions Case Study: Using BP Algorithm to Solve Handwritten Digit Recognition Problem
10. Basic Applications of TensorFlow	1. Linear Regression with TensorFlow 2. Non-Linear Regression with TensorFlow 3. Explanation of Mnist Dataset and Softmax 4. Building Handwritten Digit Recognition with BP Neural Network 5. Explanation and Use of Cross-Entropy 6. Overfitting, Regularization, Dropout 7. Various Optimizers 8. Model Saving and Loading Methods
11. Application of Convolutional Neural Networks (CNN)	1. Convolutional Neural Networks (CNN) 2. Introduction to Local Receptive Fields and Weight Sharing in Convolutions 3. Specific Calculations of Convolutions 4. Introduction to Pooling Layers (Average Pooling, Max Pooling) 5. Introduction to Same Padding and Valid Padding 6. Introduction to LeNet-5 Convolutional Network 7. Case Study: CNN Handwritten Digit Recognition
12. Application of Long Short-Term Memory Networks (LSTM)	1. Introduction to Recurrent Neural Networks (RNN) 2. Specific Calculations of RNNs 3. Introduction to Long Short-Term Memory Networks (LSTM) 4. Specific Calculations of Input Gate, Forget Gate, and Output Gate 5. Introduction to Stacked LSTMs 6. Introduction to Bidirectional LSTMs 7. LSTM Application Case Study
Day 3	13. Introduction to Common Image Recognition Models	1. AlexNet 2. VGG16 3. Inceptionv1-v4 4. ResNet 5. Inception-ResNet 6. EfficientNet-v1-v2
14. Using Pre-trained EfficientNet for Various Image Predictions	1. Downloading Pre-trained 1000-class Image Recognition Model 2. Using Pre-trained Image Recognition Model for Image Classification
15. Transfer Learning – Training Your Own Classification Model with EfficientNet	1. Data Preparation 2. Data Augmentation 3. Model Building 4. Model Training 5. Result Testing
16. Introduction to Transformer Architecture	1. Introduction to Transformer Model 2. Self-Attention 3. Multi-Head Attention
17. Introduction to Bert Model	1. Introduction to Bert Model 2. MLM and NSP Model Tasks Case Study: Using Bert Model for User Comment Classification
18. Explanation of Faster-RCNN Series Models	1. Introduction to Object Detection Projects 2. Detailed Explanation of R-CNN Model 3. Detailed Explanation of SPPNET Model 4. Detailed Explanation of Fast-RCNN Model 5. Detailed Explanation of Faster-RCNN Model 6. Implementing Faster-RCNN Object Detection with TensorFlow
19. Introduction and Application of YOLO Algorithm	1. Structure and Workflow of YOLOv1 2. Explanation of YOLOv1 Cost Function and Analysis of Its Disadvantages 3. Explanation of YOLOv2 Network Structure Darknet-19 4. YOLOv2 Precision Optimization – High Resolution and Anchor 5. YOLOv2 Precision Optimization – Dimension Clustering 6. YOLOv2 Precision Optimization – Direct Position Prediction 7. YOLOv2 Precision Optimization – Fine-Grained Features and Multi-Scale Training 8. Explanation of YOLOv3 Structure 9. Explanation of YOLOv4 Algorithm 10. Explanation of YOLOv5 Algorithm
20. Interpretation of YOLOv8 Object Detection Algorithm and Practical Project	1. Using LabelImg Object Detection Annotation Tool 2. Data Processing Project Preparation Process 3. Training Your Own Object Detection Model
21. Introduction to OCR Models	1. Introduction to OCR Algorithms 2. Using OCR Algorithms to Recognize ID Card Photos 3. Using OCR Algorithms for Rotated Photo Recognition 4. Using OCR Algorithms to Recognize Nucleic Acid Test Screenshots and Collect Relevant Information for File Saving
22. Introduction to ChatGPT	1. Introduction to ChatGPT Model Training Methods 2. Analysis of Advantages and Disadvantages of ChatGPT Applications 3. Practical Cases of ChatGPT
Supplementary Courses	1. Discuss practical problems faced by students and provide solution suggestions 2. Establish a Q&A group for students and provide post-class support 3. Provide machine learning and deep learning textbooks to gradually improve skills 4. The algorithms and models learned in training are general methods applicable to multiple industries including communication, government, healthcare, agriculture, industry, finance, meteorology, and military 5. Additional content can be added based on requirements if needed

Fee Standards

Training fees are categorized into three types, please choose flexibly based on your needs.

Type A: 3900 yuan/person (including training fee, material fee, A class certificate fee, guidance fee, invoice fee, etc.)

Certificate: Advanced “AI Application Engineer” Professional Competency Certificate issued by Zhongke Ruanyan (Beijing) Science and Technology Center, which can serve as an important basis for evaluating the capabilities of technical personnel in relevant units, salary increases, promotions, assessments, and appointments.

Type B: 4800 yuan/person (including training fee, material fee, A+B class certificate fee, guidance fee, invoice fee, etc.)

Certificate: Advanced “AI Application Engineer” Professional Competency Certificate issued by the China Management Science Research Institute’s Vocational Education Research Institute, which is included in the database of the China Management Science Research Institute and can be checked nationwide, serving as valid proof for promotions and evaluations.

Type C: Charge 5500 yuan/person (including training fee, material fee, A+B+C class certificate fee, guidance fee, invoice fee, etc.)

Certificate: Advanced “AI Application Management Engineer” Vocational Skills Certificate issued by the Ministry of Industry and Information Technology of China Communications Industry Association (national-level association), which can be checked nationwide and serves as valid proof for promotions and evaluations.

Provide official VAT invoices for easy reimbursement; if a meeting fee invoice is needed, a meeting notice can be provided.

Discount Policies

1. Students can receive a 300 yuan discount with a student ID;

2. Group registration of 3 or more people (inclusive) can receive a 200 yuan discount per person;

3. Group registration of 5 or more people (inclusive) will receive an additional free spot;

4. The above discount policies cannot be enjoyed simultaneously; only one can be applied.

Registration Method

Please scan the QR code below to register online. After successful registration, we will send you a training notice and confirm by phone.

Practical Training in AI Python Machine Learning and Deep Learning

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