本课程《用 Python 实战机器学习与生成式 AI：从数据科学到 LLM 应用开发》时长超 12 小时，由资深数据科学家亲授，面向所有对 AI、GenAI、数据科学和机器学习感兴趣的学习者，全面讲解从基础理论到项目落地的全过程。
你将系统学习如何使用 Python 构建 AI 应用、开发和微调 LLM（大语言模型）、搭建聊天机器人与 Web 应用，掌握深度学习、异常检测、图像生成、视频生成、PDF 分析、ETL、SQL、Web 爬虫、接口开发、向量数据库（如 Pinecone）等实用技能。课程结构灵活，支持按模块学习，配套完整项目与可下载源码，适合 Python 初学者、数据分析师、AI 开发者或准备转型的数据科学爱好者，带你一步步掌握构建真实 AI 应用的核心能力。
英文原版介绍
Published 7/2024
Duration: 12h 21m | Video: .MP4, 1920×1080 30 fps | Audio: AAC, 44.1kHz, 2ch | Size: 6.53 GB
Genre: eLearning | Language: English
Learn to create Machine Learning and GenAI Algorithms in Python from a Data Science expert. Code included.
What you’ll learn
Combine the power of Data Science and Machine Learning to create AI for Real-World applications
Build different AI applications within projects
Master the State of the Art Gen AI models
Create own LLM applications
How to use Python for Data Science
Requirements
First programming experience with Python (beginner level)
Description
Interested in the field of AI, Data Science, GenAI and Machine Learning?
Then this course is for you!
This course has been designed by an AI,
Data Scientist, and a Machine Learning expert
so that i can share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
I will walk you step-by-step into the
World of AI, Data Scientist, Machine Learning and GenAI.
With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, and at the same time, we dive deep into AI, Machine Learning and GenAI.
It is structured the following way
Part 1 – Intro
Part 2 – AI
Part 3 – Python
Part 4 – EDA
Part 5 – GenAI Chatbots
Part 6 – GenAI applications
Part 7 – AI Chatbots
Part 8 – Machine Learning
Part 9 – Deep Learning
Part 10 – ETL and SQL
Part 11 – Anomaly Detection (Predictive Maintenance)
Part 12- Web Crawling & Scraping
Part 13 – Image generation
Part 14 – Interfaces REST API
Part 15 – AI Agents
Part 16 – Video generation
Part 17 – ChatGPT-Data Analysis
Part 18 – ChatGPT-Developing
Part 19- Pinecone Vector Database
Part 20 – Web-Apps
Part 21 – PDF analysis
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and
learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on
real-life case projects
. So not only will you learn the theory, but you will also get lots of
hands-on practice
building your own models and applications.
And last but not least, this course
includes Python code
which you can download and use on your own projects.
What you’ll learn
Master Machine and Deep Learning on Python
Have a great intuition of many Machine Learning models
Build own GenAI applications
Use and finetune LLM models
Make powerful analysis
Use Machine Learning for personal purpose
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning and LLM model to choose for each type of problem
Build an army of powerful Machine and Deep Learning models and know how to combine them to solve any problem
Are there any course requirements or prerequisites?
Just some high school mathematics level and basic programming understanding.
Who this course is for
Anyone interested in AI, Data Science, GenAI and Machine Learning
Beginner Python developer curious about Data Science, AI and GenAI
Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
Any students in college who want to start a career in Data Science.
Any data analysts who want to level up in Machine Learning.
Any people who are not satisfied with their current job and who want to become a Data Scientist.
Students who have at least high school knowledge in math and who want to start learning Machine Learning.
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