Forwarded from Machine Learning with Python
Convert PDF to docx using Python
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses
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Forwarded from Machine Learning with Python
In the #AIPythonforBeginners course series you'll learn how to identify strings, integers, and floats with the type() function, and build a solid Python foundation for your AI journey.
Enroll Free: https://learn.deeplearning.ai/courses/ai-python-for-beginners
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Forwarded from Machine Learning with Python
Free Certification Courses to Learn Data Analytics in 2025:
1. Python
🔗 https://imp.i384100.net/5gmXXo
2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql
3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r
4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics
5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/
6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization
7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning
8. R
🔗https://imp.i384100.net/rQqomy
9. Tableau
🔗https://imp.i384100.net/MmW9b3
10. PowerBI
🔗 https://lnkd.in/dpmnthEA
11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N
12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science
13. Mathematics
🔗https://matlabacademy.mathworks.com
14. Statistics
🔗 https://lnkd.in/df6qksMB
15. Data Visualization
🔗https://imp.i384100.net/k0X6vx
16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9
17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR
18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10
19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling
20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra
21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability
22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra
23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone
24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis
25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY
26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2
27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience
1. Python
🔗 https://imp.i384100.net/5gmXXo
2. SQL
🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql
3. Statistics and R
🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r
4. Data Science: R Basics
🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics
5. Excel and PowerBI
🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/
6. Data Science: Visualization
🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization
7. Data Science: Machine Learning
🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning
8. R
🔗https://imp.i384100.net/rQqomy
9. Tableau
🔗https://imp.i384100.net/MmW9b3
10. PowerBI
🔗 https://lnkd.in/dpmnthEA
11. Data Science: Productivity Tools
🔗 https://lnkd.in/dGhPYg6N
12. Data Science: Probability
🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science
13. Mathematics
🔗https://matlabacademy.mathworks.com
14. Statistics
🔗 https://lnkd.in/df6qksMB
15. Data Visualization
🔗https://imp.i384100.net/k0X6vx
16. Machine Learning
🔗 https://imp.i384100.net/nLbkN9
17. Deep Learning
🔗 https://imp.i384100.net/R5aPOR
18. Data Science: Linear Regression
🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10
19. Data Science: Wrangling
🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling
20. Linear Algebra
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra
21. Probability
🔗 https://pll.harvard.edu/course/data-science-probability
22. Introduction to Linear Models and Matrix Algebra
🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra
23. Data Science: Capstone
🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone
24. Data Analysis
🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis
25. IBM Data Science Professional Certificate
https://imp.i384100.net/9gxbbY
26. Neural Networks and Deep Learning
https://imp.i384100.net/DKrLn2
27. Supervised Machine Learning: Regression and Classification
https://imp.i384100.net/g1KJEA
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience
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Forwarded from Machine Learning with Python
Link: https://amankharwal.medium.com/130-python-projects-with-source-code-61f498591bb
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming
https://t.iss.one/CodeProgrammer🖥
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Forwarded from Machine Learning with Python
Find your location on Map using Python
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming
https://t.iss.one/CodeProgrammer🖥
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Forwarded from Machine Learning with Python
"Introduction to Python Programming"
This 415-pages #FREE book is perfect if you are starting your #Python journey.
Download book: https://t.co/aMLeAQre6r
This 415-pages #FREE book is perfect if you are starting your #Python journey.
Download book: https://t.co/aMLeAQre6r
#DataAnalytics #DataScience #MachineLearning #DeepLearning #Statistics #Probability #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #Mathematics #PythonProgramming
https://t.iss.one/CodeProgrammer✅
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Part 3: Enterprise Web Scraping – Building Scalable, Compliant, and Future-Proof Data Extraction Systems
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-3A
Link B (Rest): https://hackmd.io/@husseinsheikho/WS-3B
Duration: ~60 minutes
Link A: https://hackmd.io/@husseinsheikho/WS-3A
Link B (Rest): https://hackmd.io/@husseinsheikho/WS-3B
#EnterpriseScraping #DataEngineering #ScrapyCluster #MachineLearning #RealTimeData #Compliance #WebScraping #BigData #CloudScraping #DataMonetization
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⚡️ How Redis counts billions of unique values while barely using memory
There's an algorithm called HyperLogLog. It allows you to roughly estimate how many unique elements have passed through the system, using about 12 KB of memory.
The idea is simple: Redis doesn't store the elements themselves.
It does the following:
- Takes an element
- Calculates a hash from it
- Uses part of the hash as a cell number
- Checks the other part to see how many consecutive zeros it contains
- If the new number is larger than the old one, it updates the cell
Why does this work?
Because a long series of zeros in the hash is rare.
For example:
- 1 consecutive zero - quite common
- 5 consecutive zeros - less common
- 10 consecutive zeros - about a 1 in 1024 chance
- 20 consecutive zeros - a very rare event
If Redis sees a very rare pattern, it means that many different elements have likely passed through it.
Redis uses 16,384 small counters. Each stores the maximum "rarity" it has seen for its group of elements.
Then Redis combines these values mathematically to get an estimate of unique elements.
Not an exact number, but a very close approximation.
The main trick of HyperLogLog:
it can handle millions or even billions of values, but memory hardly increases at all.
That's why Redis can count unique users, IPs, requests, or events without huge tables and lists.
#Redis #HyperLogLog #DataScience #Tech #BigData #MemoryEfficiency
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🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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There's an algorithm called HyperLogLog. It allows you to roughly estimate how many unique elements have passed through the system, using about 12 KB of memory.
The idea is simple: Redis doesn't store the elements themselves.
It does the following:
- Takes an element
- Calculates a hash from it
- Uses part of the hash as a cell number
- Checks the other part to see how many consecutive zeros it contains
- If the new number is larger than the old one, it updates the cell
Why does this work?
Because a long series of zeros in the hash is rare.
For example:
- 1 consecutive zero - quite common
- 5 consecutive zeros - less common
- 10 consecutive zeros - about a 1 in 1024 chance
- 20 consecutive zeros - a very rare event
If Redis sees a very rare pattern, it means that many different elements have likely passed through it.
Redis uses 16,384 small counters. Each stores the maximum "rarity" it has seen for its group of elements.
Then Redis combines these values mathematically to get an estimate of unique elements.
Not an exact number, but a very close approximation.
The main trick of HyperLogLog:
it can handle millions or even billions of values, but memory hardly increases at all.
That's why Redis can count unique users, IPs, requests, or events without huge tables and lists.
#Redis #HyperLogLog #DataScience #Tech #BigData #MemoryEfficiency
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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