Top YouTube Channels to Master Tech Skills 🚀

1. SQL 💻
👉 youtube.com/@joeyblue1

2. Excel 📊
👉 youtube.com/@excelisfun

3. Statistics 📈
👉 youtube.com/@statquest

4. Math 🧮
👉 youtube.com/results?search…

5. Python 🐍
👉 youtube.com/@BroCodez

6. Data Analysis 🔍
👉 youtube.com/@AlexTheAnalyst

7. Machine Learning 🤖
👉 youtube.com/@campusx-offic…

8. Deep Learning 🧠
👉 youtube.com/@deeplizard

9. Java ☕
👉 youtube.com/@Telusko

10. Big Data 📦
👉 youtube.com/@thedatatech

11. Data Engineering ⚙️
👉 youtube.com/@dataengineeri…

12. NLP (Natural Language Processing) 🗣️
👉 youtube.com/@codebasics

13. Computer Vision & AI 👁️
👉 youtube.com/@murtazasworks…

14. Generative AI ✨
👉 youtube.com/@sunnysavita10

15. University-Level Courses 🎓
👉 youtube.com/@stanfordonline
👉 youtube.com/@mitocw

16. All-in-One Learning 📚
👉 youtube.com/@freecodecamp

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🔖 A useful training tool for Data Scientists 📊

🫡 Real-world tasks from IT companies;
🫡 SQL practice;
🫡 Python tasks;
🫡 Preparation for Data Science interviews.

⛓ Link to the training tool
https://www.stratascratch.com/

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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

#Pandas #Python #DataScience #Performance #Fireducks #BigData

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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖

https://github.com/visenger/awesome-mlops

#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources

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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖

We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️

⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

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Python And Flask Framework Complete Course

Python-Powered Proficiency: Depth Introduction To Python Programming And Python Web Framework Flask.…

🏷 Category: it-and-software
🌍 Language: English (US)
👥 Students: 292,248 students
⭐️ Rating: 4.5/5.0 (2,187 reviews)
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💎 By: https://t.iss.one/Udemy26
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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

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DS FULL ARCHIVE 🗂

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