Python AI Masterclass

Python AI Applications & Deep Learning Masterclass

Integrating AI into daily office data, from classical classifiers to the Transformer revolution.

Designed for professionals looking to apply cutting-edge AI technology to their daily work, covering deep practices from ML theory to LLM architectures.

First Cohort AI Masterclass Offer!

Limited seats available. Contact us now for exclusive offers and demo class info for the first AI cohort.

Course Introduction

In the AI era, mastering how to combine AI technology with daily office data is key to enhancing competitiveness. This course leads students deep into the core of AI, starting from basic supervised and unsupervised learning to practical applications of classifiers like KNN. We explore advanced issues in neural network training such as overfitting and gradient vanishing, providing professional solutions. The highlight is a deep dive into the Transformer architecture—the foundation of modern AI like ChatGPT. Students will learn how to use these technologies to handle tedious office tasks and achieve true AI office automation.

Course Syllabus (Total 8 Hours)

Session 1: ML Cores & Classifier Practice (2 Hours) • Analysis of Supervised vs. Unsupervised Learning • Classical Classifier Application: KNN (K-Nearest Neighbors) explained • The art of dataset splitting: How to scientifically divide Train / Val / Test Sets • Case Study: Using KNN for office automation classification tasks
Session 2: Deep Learning Optimization & Neural Network Training (2 Hours) • Core concepts of ANN training: Weight updates and loss functions • Solving model pain points: Overfitting and Gradient Vanishing • Handling techniques: Dropout, Batch Norm, and optimizer selection • Case Study: Optimizing office prediction models to solve generalization issues
Session 3: The Transformer Revolution & Architecture Analysis (2 Hours) • Why Transformer? The evolution from RNN/CNN to Attention • Core components: Self-Attention and Encoder-Decoder architecture • Basic working principles of Large Language Models (LLMs) • Case Study: Analyzing the absolute advantage of Transformers in text processing
Session 4: AI Scenarios in Office Data (2 Hours) • Specific application scenarios for Transformers in daily office tasks (summaries, auto-replies, sentiment analysis) • Using pre-trained models to solve domain-specific problems (Fine-tuning concepts) • Hands-on: Building an AI-based office assistant to process massive daily documents

Suitable for students who want to deeply understand AI principles, master deep learning optimization techniques, and apply them to actual office scenarios.