You said:
H
ChatGPT said:
Hey! 👋
What’s up?
You said:
path between python data science machine Learning deep learning nueral network and ai.is there a linere relation between them?I mean I start from python and continue forward to the end and passing from all if them
And from Udemy introduce me the courses from beginning to the end with highest score and popularity
ChatGPT said:
Yes — there is a clear progression (a “roadmap”) from learning Python to mastering Artificial Intelligence (AI), and many learners follow it step by step. Below I’ll explain the relationship between these topics and then give you a course path from beginner to advanced using the highest-rated and most popular Udemy courses.
🧠 How These Topics Fit Together
Here’s the typical learning path from Python → AI:
Python Programming
Python is the foundation. It’s the lingua franca of data science, machine learning (ML), and AI.
Data Science
Using Python to clean and analyze data (Pandas, NumPy, visualization). This prepares you for ML.
Machine Learning (ML)
ML teaches computers to learn patterns and make predictions from data (Scikit-Learn, regression, classification).
Deep Learning (DL)
A subset of ML focused on neural networks (TensorFlow, PyTorch). This is where powerful AI systems like image & speech recognition come from.
Neural Networks
The core of deep learning — models inspired by the human brain.
Artificial Intelligence (AI)
The broadest field. ML + DL are major parts of modern AI, but AI also includes logic, search, and robotics.
So yes — you start with Python, then Data Science, then Machine Learning, then Deep Learning and Neural Networks, and finally broader AI topics as you get more advanced.
🎓 Recommended Udemy Skill Path (Beginner → Advanced)
Below is a recommended sequence of courses that builds your skills step-by-step, with high ratings and large student counts.
🔹 1. Python + Data Science Fundamentals
📌 Python for Data Science and Machine Learning Bootcamp by Jose Portilla
Learn Python basics + NumPy, Pandas, visualization, and introductory ML
Very popular and beginner friendly with lots of hands-on practice
Why this first: You get strong Python skills and start touching data science & ML basics.
🔹 2. Core Machine Learning
📌 Machine Learning A-Z™ (Python & R) by SuperDataScience Team
Covers classic algorithms (regression, classification, clustering)
Practical, step-by-step with real examples
Why this next: It bridges data science and AI by teaching you to build ML models.
🔹 3. Deep Learning & Neural Networks
📌 Deep Learning A-Z™: Hands-On Artificial Neural Networks
Teaches Deep Learning fundamentals and key network types
Works with Python frameworks (TensorFlow / Keras)
📌 Data Science: Deep Learning and Neural Networks in Python (Lazy Programmer)
Builds neural networks from scratch and with TensorFlow
🔹 4. AI / Full Data Science Career Track
📌 Data Science & AI Masters 2025 — From Python to Gen AI
A more complete journey that includes deep learning, NLP, and generative AI
📌 Data Science & AI Mastery: From Basics to Deployment
Adds real project and deployment experience
These bigger courses link everything into a real-world AI career path.
H
ChatGPT said:
Hey! 👋
What’s up?
You said:
path between python data science machine Learning deep learning nueral network and ai.is there a linere relation between them?I mean I start from python and continue forward to the end and passing from all if them
And from Udemy introduce me the courses from beginning to the end with highest score and popularity
ChatGPT said:
Yes — there is a clear progression (a “roadmap”) from learning Python to mastering Artificial Intelligence (AI), and many learners follow it step by step. Below I’ll explain the relationship between these topics and then give you a course path from beginner to advanced using the highest-rated and most popular Udemy courses.
🧠 How These Topics Fit Together
Here’s the typical learning path from Python → AI:
Python Programming
Python is the foundation. It’s the lingua franca of data science, machine learning (ML), and AI.
Data Science
Using Python to clean and analyze data (Pandas, NumPy, visualization). This prepares you for ML.
Machine Learning (ML)
ML teaches computers to learn patterns and make predictions from data (Scikit-Learn, regression, classification).
Deep Learning (DL)
A subset of ML focused on neural networks (TensorFlow, PyTorch). This is where powerful AI systems like image & speech recognition come from.
Neural Networks
The core of deep learning — models inspired by the human brain.
Artificial Intelligence (AI)
The broadest field. ML + DL are major parts of modern AI, but AI also includes logic, search, and robotics.
So yes — you start with Python, then Data Science, then Machine Learning, then Deep Learning and Neural Networks, and finally broader AI topics as you get more advanced.
🎓 Recommended Udemy Skill Path (Beginner → Advanced)
Below is a recommended sequence of courses that builds your skills step-by-step, with high ratings and large student counts.
🔹 1. Python + Data Science Fundamentals
📌 Python for Data Science and Machine Learning Bootcamp by Jose Portilla
Learn Python basics + NumPy, Pandas, visualization, and introductory ML
Very popular and beginner friendly with lots of hands-on practice
Why this first: You get strong Python skills and start touching data science & ML basics.
🔹 2. Core Machine Learning
📌 Machine Learning A-Z™ (Python & R) by SuperDataScience Team
Covers classic algorithms (regression, classification, clustering)
Practical, step-by-step with real examples
Why this next: It bridges data science and AI by teaching you to build ML models.
🔹 3. Deep Learning & Neural Networks
📌 Deep Learning A-Z™: Hands-On Artificial Neural Networks
Teaches Deep Learning fundamentals and key network types
Works with Python frameworks (TensorFlow / Keras)
📌 Data Science: Deep Learning and Neural Networks in Python (Lazy Programmer)
Builds neural networks from scratch and with TensorFlow
🔹 4. AI / Full Data Science Career Track
📌 Data Science & AI Masters 2025 — From Python to Gen AI
A more complete journey that includes deep learning, NLP, and generative AI
📌 Data Science & AI Mastery: From Basics to Deployment
Adds real project and deployment experience
These bigger courses link everything into a real-world AI career path.
Data science Road Map
Python1-2 M
R 1-2 Month
is also useful and powerful and easy language for data features
Git. 1-2weeks
Data Structure and algorithms 1-2 M
SQL for database manipulation 1-2M
Math (linear algebra /calculus/probability/statistics)
Data processing and visualization
Business tools(tableau. Power BI)very useful
ML
(Supervised /unsupervised)
(Libraries: Tenserflow / pytorch /scikit learn)
Deep learning
(deep Neural networks: for image and speak recognition / more advanced: CNNs , RNNs
Deep Learning subsets: NLP , COMPUTER VISION
BIG DATA(Hadoop , spark)
Daily (3-5 h) >>> 12 - 20 months
Python1-2 M
R 1-2 Month
is also useful and powerful and easy language for data features
Git. 1-2weeks
Data Structure and algorithms 1-2 M
SQL for database manipulation 1-2M
Math (linear algebra /calculus/probability/statistics)
Data processing and visualization
Business tools(tableau. Power BI)very useful
ML
(Supervised /unsupervised)
(Libraries: Tenserflow / pytorch /scikit learn)
Deep learning
(deep Neural networks: for image and speak recognition / more advanced: CNNs , RNNs
Deep Learning subsets: NLP , COMPUTER VISION
BIG DATA(Hadoop , spark)
Daily (3-5 h) >>> 12 - 20 months
نصب اکستنشن ها مثل ai gemini chatgpt یا غیره رو vscode یا گوگل کولب برای تعاملی کد زدن و بهینه کار کردن
# 🚀 Data Science & ML Mastery: Top 20 Skills (2026)
This roadmap takes you from beginner to expert with 100% free, high-quality resources.
| # | Skill | Proficiency | Resource Link |
| :--- | :--- | :--- | :--- |
| 1 | Python Programming |
Beginner | [Kaggle Learn](https://www.kaggle.com/learn/python) |
| 2 | Linear Algebra & Calculus |
Beginner | [3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3Mne08OtBDPsE22yz4Os) |
| 3 | Statistics & Probability |
Beginner | [StatQuest](https://www.youtube.com/c/joshstarmer) |
| 4 | SQL & Data Querying |
Beginner | [SQLZoo Interactive](https://sqlzoo.net/) |
| 5 | Data Manipulation
| Beginner | [Pandas Official Guide](https://pandas.pydata.org/docs/getting_started/index.html) |
| 6 | Exploratory Data Analysis |
Beginner | [Google Data Prep](https://developers.google.com/machine-learning/data-prep) |
| 7 | Data Visualization |
Intermediate | [Fundamentals of Data Viz](https://clauswilke.com/dataviz/) |
| 8 | Classical Machine Learning |
Intermediate | [Google ML Crash Course](https://developers.google.com/machine-learning/crash-course) |
| 9 | Feature Engineering |
Intermediate | [Kaggle Learn](https://www.kaggle.com/learn/feature-engineering) |
| 10 | Model Evaluation |
Intermediate | [Scikit-Learn Guide](https://scikit-learn.org/stable/modules/model_evaluation.html) |
| 11 | Deep Learning Foundations |
Intermediate | [Fast.ai Course](https://course.fast.ai/) |
| 12 | NLP
| Intermediate | [Hugging Face Course](https://huggingface.co/learn/nlp-course) |
| 13 | Computer Vision
| Advanced | [Stanford CS231n](http://cs231n.stanford.edu/) |
| 14 | Time Series Analysis
| Advanced | [FPP3 Forecasting](https://otexts.com/fpp3/) |
| 15 | Big Data (PySpark) |
Advanced | [IBM Spark Fundamentals](https://cognitiveclass.ai/courses/what-is-spark) |
| 16 | MLOps & Deployment |
Advanced | [ML Zoomcamp](https://github.com/DataTalksClub/machine-learning-zoomcamp) |
| 17 | Generative AI Engineering |
Expert | [Microsoft GenAI](https://github.com/microsoft/generative-ai-for-beginners) |
| 18 | RAG & Vector DBs
| Expert | [Pinecone Learning](https://www.pinecone.io/learn/) |
| 19 | AI Agent Orchestration
| Expert | [DeepLearning.ai Agents](https://www.deeplearning.ai/short-courses/ai-agents-in-langchain/) |
| 20 | AI Ethics & Governance
| Expert | [Google ML Fairness](https://developers.google.com/machine-learning/fairness-overview) |
---
*Roadmap curated for the 2026 Data Frontier.*
This roadmap takes you from beginner to expert with 100% free, high-quality resources.
| # | Skill | Proficiency | Resource Link |
| :--- | :--- | :--- | :--- |
| 1 | Python Programming |
Beginner | [Kaggle Learn](https://www.kaggle.com/learn/python) |
| 2 | Linear Algebra & Calculus |
Beginner | [3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3Mne08OtBDPsE22yz4Os) |
| 3 | Statistics & Probability |
Beginner | [StatQuest](https://www.youtube.com/c/joshstarmer) |
| 4 | SQL & Data Querying |
Beginner | [SQLZoo Interactive](https://sqlzoo.net/) |
| 5 | Data Manipulation
| Beginner | [Pandas Official Guide](https://pandas.pydata.org/docs/getting_started/index.html) |
| 6 | Exploratory Data Analysis |
Beginner | [Google Data Prep](https://developers.google.com/machine-learning/data-prep) |
| 7 | Data Visualization |
Intermediate | [Fundamentals of Data Viz](https://clauswilke.com/dataviz/) |
| 8 | Classical Machine Learning |
Intermediate | [Google ML Crash Course](https://developers.google.com/machine-learning/crash-course) |
| 9 | Feature Engineering |
Intermediate | [Kaggle Learn](https://www.kaggle.com/learn/feature-engineering) |
| 10 | Model Evaluation |
Intermediate | [Scikit-Learn Guide](https://scikit-learn.org/stable/modules/model_evaluation.html) |
| 11 | Deep Learning Foundations |
Intermediate | [Fast.ai Course](https://course.fast.ai/) |
| 12 | NLP
| Intermediate | [Hugging Face Course](https://huggingface.co/learn/nlp-course) |
| 13 | Computer Vision
| Advanced | [Stanford CS231n](http://cs231n.stanford.edu/) |
| 14 | Time Series Analysis
| Advanced | [FPP3 Forecasting](https://otexts.com/fpp3/) |
| 15 | Big Data (PySpark) |
Advanced | [IBM Spark Fundamentals](https://cognitiveclass.ai/courses/what-is-spark) |
| 16 | MLOps & Deployment |
Advanced | [ML Zoomcamp](https://github.com/DataTalksClub/machine-learning-zoomcamp) |
| 17 | Generative AI Engineering |
Expert | [Microsoft GenAI](https://github.com/microsoft/generative-ai-for-beginners) |
| 18 | RAG & Vector DBs
| Expert | [Pinecone Learning](https://www.pinecone.io/learn/) |
| 19 | AI Agent Orchestration
| Expert | [DeepLearning.ai Agents](https://www.deeplearning.ai/short-courses/ai-agents-in-langchain/) |
| 20 | AI Ethics & Governance
| Expert | [Google ML Fairness](https://developers.google.com/machine-learning/fairness-overview) |
---
*Roadmap curated for the 2026 Data Frontier.*
Kaggle
Learn Python Tutorials
Learn the most important language for data science.
Analyzing Video Games Matches like Dota 2,lol,and other games and gaining insights from hidden patterns in games and players behaviours
وقتی روی چیزی تمرکز گردید که نمیخواهید و حس بدی به شما می دهد به وضوح میتواند به شما بگوید که چه چیزی میخواهید وقتی که از ابتدا خواسته مشخص و شفافی در ذهن ندارین
مانند استخدام تو شرکت ها با اون حقوق ها و اون پیش نیاز ها برای یه متخصص که حقوق یه کارمند میان رده رو بگیره
کلا زیر دستی یا استخدامی هیچ وقت گزینه خوبی نیس
کلا زیر دستی یا استخدامی هیچ وقت گزینه خوبی نیس
Resources | Linear Algebra | Mathematics | MIT OpenCourseWare
https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/download/
https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/download/
MIT OpenCourseWare
Resources | Linear Algebra | Mathematics | MIT OpenCourseWare
MIT OpenCourseWare is a web based publication of virtually all MIT course content. OCW is open and available to the world and is a permanent MIT activity