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

سوالات مصاحبه های شغلی حوزه Machine Learning

https://github.com/khangich/machine-learning-interview

#interview #Machine_Learning



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

اگه میخواهید به مبحث آمار و احتمال برای ورود به حوزه یادگیری ماشین مسلط شوید پیشنهاد میکنم از این پلی لیست استفاده کنید

https://www.youtube.com/playlist?list=PLBh2i93oe2qswFOC98oSFc37-0f4S3D4z



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

معرفی دو پلی لیست خوب از MIT برای یادگیری جبر خطی

https://www.youtube.com/watch?v=7UJ4CFRGd-U&list=PL221E2BBF13BECF6C&index=1


https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D


🆔 @programmers_street

یه پلی لیست خوب برا یادگیری Deep Learning

https://www.youtube.com/playlist?list=PLblh5JKOoLUIxGDQs4LFFD--41Vzf-ME1


#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

Linear Algebra was taught using Sheldon Axler's book for Mtech AI at IISc.
The book's author has made a course on Linear Algebra and released it for free!

https://www.youtube.com/playlist?list=PLGAnmvB9m7zOBVCZBUUmSinFV0wEir2Vw



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

A nice resource by OpenAI to learn Deep Reinforcement Learning

https://github.com/openai/spinningup?tab=readme-ov-file


#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

Full Stack Deep Learning course for coders

https://www.youtube.com/playlist?list=PL1T8fO7ArWleMMI8KPJ_5D5XSlovTW_Ur



#یادگیری_ماشین #Machine_Learning

🆔 @programmers_street

اگه دنبال تمرین و یادگیری مسائل ماشین لرنینگ هستید بخصوص برای مصاحبه و شغل این وبسایت جالبه! کلی سوال داره که از ساده تا سخت دسته بندی شده و میتونید تمرین کنید.

Link: https://www.deep-ml.com/


#یادگیری_ماشین #interview

معرفی منابع آموزشی یادگیری ماشین 👇👇

🆔 @programmers_street

بهترین چنل های یوتیوب برای یادگیری پایتون در سال ۲۰۲۵

❯ Python
https://youtube.com/playlist?list=PL-osiE80TeTt2d9bfVyTiXJA-UTHn6WwU

❯ Django
https://youtube.com/playlist?list=PL4cUxeGkcC9iqfAag3a_BKEX1N43uJutw

❯ Flask
https://www.youtube.com/playlist?list=PL7yh-TELLS1EyAye_UMnlsTGKxg8uatkM

❯ FastAPI
https://www.youtube.com/playlist?list=PLK8U0kF0E_D6l19LhOGWhVZ3sQ6ujJKq_

❯ Numpy
https://www.youtube.com/playlist?list=PLCC34OHNcOtpalASMlX2HHdsLNipyyhbK

❯ Pandas
https://www.youtube.com/playlist?list=PLCC34OHNcOtqSz7Ke7kaYRf9CfviJgO55

❯ Scikit-Learn
https://www.youtube.com/playlist?list=PLcQVY5V2UY4LNmObS0gqNVyNdVfXnHwu8

#پایتون

🆔 @programmers_street

دوره ی 5 روزه ی گوگل (رایگان) برای یادگیری GenAI برای developerها...

سرفصل هاش بسیار جالبه یه پروژه ی پایانی هم بهتون میدن که انجام بدین...

https://rsvp.withgoogle.com/events/google-generative-ai-intensive_2025q1


🆔 @programmers_street

دوره آموزش بازی سازی موبایلی را امروز با قیمت باورنکردنی با ۹۵٪ تخفیف تهیه کنید

دوره برای ده نفر اول ۹۵ درصد تخفیف داره
بعد از اون تخفیف کم میشه
👇👇👇

zaya.io/jhwfo

با این دوره یه مهارت  جالب و جذاب رو ياد بگیرید وبه بازار پرسود ساخت بازی برای موبایل وارد بشید

The Generative AI Learning Roadmap
Generative AI is a type of AI that can create new content based on what it has learned from existing knowledge. It has the potential to revolutionize human learning.


1- Learn about important concepts like Probability, Statistics, Calculus, and Linear Algebra.

2- Understand the working of foundational models like GPT, MetaAI’s Llama, Gemini, DeepSeek, and Claude.

3- Learn the GenAI development stack that includes Python, Language, ChatGPT APIs, Prompt Engineering, VectorDB, DeepSeek, Llama, and Huggingface.

4- Learn how to train and fine-tune a foundation model.

5- Understand the role of AI Agents and how to build one using GenAI tools.

6- Learn about GenAI models for computer vision such as GAN (Generative Adversarial Networks), MidJourney, DALL E, Flux, and so on.

7 - Make use of GenAI Learning Resources such as DeepLearning AI platform, Kaggle, Generative AI Insider’s Guide by ByteByteGo, Google Labs, and Nvidia Learning platforms.

🆔 @programmers_street