سایت های معروف برای تمرین و یادگیری برنامه‌نویسی و
Popular Learning Sites

- https://w3schools.com
- https://tutorialspoint.com
- https://geeksforgeeks.org
- https://programiz.com
- https://freecodecamp.org/learn
- https://kaggle.com/learn
- https://towardsdatascience.com


DevOps

- https://www.atlassian.com/devops
- https://dzone.com/devops-and-cicd
- https://www.theserverside.com/resources/DevOps-driven-cloud-native-app-development
- https://devopscube.com/
- https://www.infoworld.com/devops/


دانشکده مهندسی کامپیوتر 👇👇

🆔 @programmers_street

نویسنده کتاب مشهور The Algorithm Design Manual ویدئوهای درس الگوریتمش رو در یوتیوب گذاشته:
https://www.youtube.com/playlist?list=PLOtl7M3yp-DXbHTFe_w9zFPXeau28CDao

Course Website: https://www3.cs.stonybrook.edu/~skiena/373/

Lecture notes, videos, and example sheets: https://www.cl.cam.ac.uk/teaching/2021/Algorithms/materials.html



#علم_داده #DataScience
دانشکده مهندسی کامپیوتر 👇👇

🆔 @programmers_street

اگه میخواهید مبحث یادگیری عمیق با پایتون رو خوب یاد بگیرید پیشنهاد میکنم از این کتاب استفاده کنید
این لینک حاوی کتاب آموزشی و notebook های پایتونه که مربوط به کدهای داخل کتابه.


https://udlbook.github.io/udlbook



#یادگیری_ماشین #Machine_Learning
دانشکده مهندسی کامپیوتر 👇👇

🆔 @programmers_street

اگه دوست دارید یادبگیرید چطور agent های خودتون را درست کنید و دیپلوی کنید، کمپانی HuggingFace یک دوره رایگان گذاشته. این دوره از یادگیری مفاهیم اصلی شروع میشه و بعد کار با فریمورک های مختلف مثل langchain و llamaIndex و Smolagents را یاد میدن. و آخر هم که چندین مثال واقعی استفاده از agent ها را یاد میگیرید. به نظر میاد که certificate هم میدن (که البته به نظر من دادن یا ندادن اون اصلا مهم نیست!). یکی از بهترین دوره های کاربردی میتونه باشه!
تو این لینک برید و ثبت نام کنید.
Link: https://huggingface.us17.list-manage.com/subscribe?u=7f57e683fa28b51bfc493d048&id=9ed45a3ef6


#یادگیری_ماشین #Machine_Learning
دانشکده مهندسی کامپیوتر 👇👇

🆔 @programmers_street

MIT Courses for Comp Sc & Machine Learning

❯ 6.042J - Mathematics for Computer Science
❯ 6.100L - Programming in Python
❯ 6.006 - Introduction to Algorithms
❯ 6.036 - Introduction to ML
❯ 6.S191 - Introduction to DL
❯ 6.034 - AI
❯ 6.5830 - DBMS
❯ 6.1810 - OS
❯ 14.15J - Networking

❯ 18.01 - Single Variable Calculus
❯ 18.02 - Multi Variable Calculus
❯ 18.05 - Introduction to Probability and Statistics
❯ 18.06 - Linear Algebra
❯ 6.092 - Programming in Java
❯ 6.S096 - C and C++
❯ 6.867 - Advanced ML
❯ 6.875 - Cryptography
❯ 6.045J - Automata Theory
❯ 6.046J - Design and Analysis of Algorithms
❯ 6.851 - Advanced Data Structures
❯ 6.852J - Distributed Algorithms
❯ 6.854J - Advanced Algorithms
❯ 18.657 - Mathematics of Machine Learning
❯ 18.S191 - Introduction to Computational Thinking
❯ 18.S096 - Matrix Calculus for ML

🔗Browse all free courses from here:

https://ocw.mit.edu/search/



#یادگیری_ماشین #Machine_Learning
دانشکده مهندسی کامپیوتر 👇👇

🆔 @programmers_street

An open source headless browser rewritten in Zig designed for AI and automation.

• 9x less memory footprint than Chrome
• 11x faster execution than Chrome


https://github.com/lightpanda-io/browser

🆔 @programmers_street

منابع رایگان از موسسات معتبر بری یادگیری علم داده در سال 2025:

- Harvard
- Stanford
- MIT
- Microsoft
- Google
- IBM

10 Top Courses w/ Certificates* :

1. Python

    - From Harvard

https://cs50.harvard.edu/python/


2. SQL

     - From Stanford Online
https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

3. Mathematics

     - From MIT OCW
https://ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum

4. Excel

    - From Microsoft Learn
https://learn.microsoft.com/training/paths/modern-analytics/


5. PowerBI

    - From Microsoft Learn
https://learn.microsoft.com/collections/m14nt4rdwnwp04


6. Tableau

https://tableau.com/learn/training

7. Data Visualization

     - From Harvard
https://pll.harvard.edu/course/data-science-visualization

8. Data Analysis

     - From IBM
https://skillsbuild.org/adult-learners/explore-learning/data-analyst

9. Data Science using Python
https://learn.saylor.org/course/view.php?id=504

10. Machine Learning

      - From Google
https://developers.google.com/machine-learning/crash-course

#علم_داده #DataScience

🆔 @programmers_street

یک نقشهٔ راهِ عالی برایِ یادگیریِ ریاضیات پیدا کردم!
خوبی‌ش اینه برایِ هر موضوعی بهترین منبعِ مطالعاتی‌ش هم معرفی کرده. توضیحاتِ ریدمی هم بخونید نکاتِ خوبی داره.
https://github.com/TalalAlrawajfeh/mathematics-roadmap



🆔 @programmers_street

لیستی از دوره های رایگان Computer Science موسسات معتبر

در هر سطحی که هستید برای حرفه ای شدن و افزایش مهارتهاتون در زمینه هوش‌مصنوعی، علم داده و یادگیری ماشین از این آموزشها استفاده کنید

- Harvard
- Stanford
- MIT
- IIT
- Microsoft
- Google

10 Top Courses :

1. Introduction with CS50

    - From Harvard

https://cs50.harvard.edu/x/

2. Computer Organization and Architecture

    - From IIT Delhi
https://onlinecourses.nptel.ac.in/noc24_cs83/preview

3. Mathematics for Comp Sc.

    - From MIT
https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-fall-2010/

4. Programming Languages

Learn at least one language.

❯ Python
https://developers.google.com/edu/python

❯ C/C++
https://ocw.mit.edu/courses/6-s096-effective-programming-in-c-and-c-january-iap-2014/

❯ Java
https://learn.microsoft.com/shows/java-for-beginners/

❯ C#
https://learn.microsoft.com/collections/yz26f8y64n7k07

5. Data Structures and Algorithms

    - From MIT

❯ Introduction to Algorithms
https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-spring-2020/

❯ Design and Analysis of Algorithms
https://ocw.mit.edu/courses/6-046j-design-and-analysis-of-algorithms-spring-2015/


6. Operating System

    - From MIT
https://ocw.mit.edu/courses/6-1810-operating-system-engineering-fall-2023/


7. DBMS

    - From Stanford
https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql


8. Computer Networks

    - From MIT
https://ocw.mit.edu/courses/14-15j-networks-spring-2018/


9. Software Engineering

    - From Google
https://techdevguide.withgoogle.com/paths/principles/


10. Advanced Topics

❯ Cryptography
https://ocw.mit.edu/courses/6-875-cryptography-and-cryptanalysis-spring-2005/

❯ Automata Theory
https://ocw.mit.edu/courses/6-045j-automata-computability-and-complexity-spring-2011/

❯ Computation Structures
https://ocw.mit.edu/courses/6-004-computation-structures-spring-2009/

❯ Artificial Intelligence
https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/

#پایتون #علم_داده #هوش_مصنوعی #یادگیری_ماشین

🆔 @programmers_street

۱۱۵۰ سوال مصاحبه‌های ماشین لرنینگ

https://mkareshk.github.io/ml-interview


#یادگیری_ماشین #Machine_Learning
#interview


🆔 @programmers_street

Open-source search engine API

🔗https://github.com/meilisearch/meilisearch


🆔 @programmers_street

Open-source low-code app builder

🔗 https://github.com/appsmithorg/appsmith



معرفی منابع آموزشی مهندسی کامپیوتر 👇👇
📲 @programmers_street

یه pdf عالی برای یادگیری ماشین لرنینگ از Andrew Ng
“Machine Learning yearning”

An introductory FREE book about developing ML algorithms by Andrew Ng.

🔗https://home-wordpress.deeplearning.ai/wp-content/uploads/2022/03/andrew-ng-machine-learning-yearning.pdf



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

Stanford’s CS229 - Machine Learning

A complete lecture notes by Andrew Ng (227 pages).

https://cs229.stanford.edu/main_notes.pdf



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

You can now learn Data Science, FREE:

- Harvard
- Stanford
- MIT
- Google
- Microsoft
- IBM

10 Top Courses with Certificates* :

1. Python

    - From Harvard

https://cs50.harvard.edu/python/


2. SQL

     - From Stanford Online

https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql


3. Excel

    - From Microsoft Learn

https://learn.microsoft.com/training/paths/modern-analytics/


4. Power BI

    - From Microsoft Learn

https://learn.microsoft.com/collections/m14nt4rdwnwp04


5. Data Visualization

     - From Harvard

https://pll.harvard.edu/course/data-science-visualization


6. Data Analysis

     - From IBM SkillsBuild

https://skillsbuild.org/adult-learners/explore-learning/data-analyst


7. Data Science using Python

    - From Saylor Academy [Complete Course]

https://learn.saylor.org/course/view.php?id=504


8. Machine Learning

      - From Google

https://developers.google.com/machine-learning/crash-course


9. Deep Learning

      - From MIT

https://introtodeeplearning.com


10. Mathematics

     - From MIT

https://ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum



#علم_داده #Data_Science

🆔 @programmers_street

🔗 Basics of Machine Learning 👇👇

Machine learning is a branch of artificial intelligence where computers learn from data to make decisions without explicit programming. There are three main types:

1. Supervised Learning: The algorithm is trained on a labeled datasets, learning to map input to output. For example, it can predict housing prices based on features like size and location.

2. Unsupervised Learning: The algorithm explores data patterns without explicit labels. Clustering is a common task, grouping similar data points. An example is customer segmentation for targeted marketing.

3. Reinforcement Learning: The algorithm learns by interacting with an environment. It receives feedback in the form of rewards or penalties, improving its actions over time. Gaming AI and robotic control are applications.

📖 Key concepts include:

- Features and Labels: Features are input variables, and labels are the desired output. The model learns to map features to labels during training.

- Training and Testing: The model is trained on a subset of data and then tested on unseen data to evaluate its performance.

- Overfitting and Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, performing poorly on new data. Underfitting happens when the model is too simple and fails to capture the underlying patterns.

- Algorithms: Different algorithms suit various tasks. Common ones include linear regression for predicting numerical values, and decision trees for classification tasks.

In summary, machine learning involves training models on data to make predictions or decisions. Supervised learning uses labeled data, unsupervised learning finds patterns in unlabeled data, and reinforcement learning learns through interaction with an environment. Key considerations include features, labels, overfitting, underfitting, and choosing the right algorithm for the task.



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

Generative AI course for beginners

🔗 https://github.com/microsoft/generative-ai-for-beginners

#هوش_مصنوعی

🆔 @programmers_street

آموزش یادگیری ماشین در ۱۰۰ روز


این ریپو یه برنامه ی ۱۰۰ روزه برای تمرین کدنویسی و یادگیری ماشین به شما می ده

https://github.com/Avik-Jain/100-Days-Of-ML-Code



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street