Artificial Intelligence Masterclass with Python : 1

Python人工智能大师课：从零到实战

2025全新发布，6.1GB视频教程，18小时+从零学习AI。涵盖机器学习、深度学习、强化学习，实战项目驱动，无需AI基础，附赠Python入门。

Published 9/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 18h 37m | Size: 6.1 GB

Learn AI from scratch with hands-on projects: Machine Learning, Deep Learning, Reinforcement Learning

What you’ll learn
Understand the foundational math behind AI, including linear algebra, probability, and optimization.
Build and train machine learning models from scratch using Python and PyTorch.
Develop deep learning systems such as CNNs, RNNs, Transformers, and Autoencoders with real code.
Apply reinforcement learning algorithms including SARSA, Q-learning, PPO, and A3C in interactive environments.
Use techniques like PCA, regularization, and cross-validation to improve model performance.
Explore advanced topics such as Graph Neural Networks, Bayesian methods, and Meta-Learning with working examples.

Requirements
No prior background in AI is required.
Basic programming knowledge helps, but there’s an optional Python section at the beginning for anyone who needs it.
You’ll need a computer that can run Python and a stable internet connection to follow along with the tools and notebooks.

Description
This course is built for learners who want a serious, structured path into Artificial Intelligence. Whether you’re coming from engineering, programming, or analytics — or even starting from scratch — you’ll find that everything here is laid out in a practical, step-by-step format.We start with foundational math and basic Python — so you don’t have to worry if you haven’t used linear algebra or probability in a while. You’ll get clear walkthroughs of the math behind algorithms, with Python implementations that you can run, change, and learn from directly.From there, we cover all the major building blocks of modern AI:Supervised and unsupervised learningModel accuracy and regularizationDeep learning with CNNs, RNNs, and TransformersReinforcement learning methods like Q-Learning, PPO, A3C, TRPOBayesian models, optimization methods, and neural architecture searchYou’ll work with real code, solve tasks visually, and understand why each method works — not just how to use it. We also use a mix of Python, PyTorch, Julia, and Colab notebooks where appropriate.If you’re looking for an over-the-top promo, you won’t find it here. This course is detailed, technical, and designed to make sure you walk away actually understanding AI.All content is developed and presented by Advancedor Academy.