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Data Science Machine Learning Data Analysis
37.9K subscribers
Data Science Machine Learning Data Analysis
Forwarded from Python | Machine Learning | Coding | R
Free Certification Courses to Learn Data Analytics in 2025:

1. Python
๐Ÿ”— https://imp.i384100.net/5gmXXo

2. SQL
๐Ÿ”— https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql

3. Statistics and R
๐Ÿ”— https://edx.org/learn/r-programming/harvard-university-statistics-and-r

4. Data Science: R Basics
๐Ÿ”—https://edx.org/learn/r-programming/harvard-university-data-science-r-basics

5. Excel and PowerBI
๐Ÿ”— https://learn.microsoft.com/en-gb/training/paths/modern-analytics/

6. Data Science: Visualization
๐Ÿ”—https://edx.org/learn/data-visualization/harvard-university-data-science-visualization

7. Data Science: Machine Learning
๐Ÿ”—https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

8. R
๐Ÿ”—https://imp.i384100.net/rQqomy

9. Tableau
๐Ÿ”—https://imp.i384100.net/MmW9b3

10. PowerBI
๐Ÿ”— https://lnkd.in/dpmnthEA

11. Data Science: Productivity Tools
๐Ÿ”— https://lnkd.in/dGhPYg6N

12. Data Science: Probability
๐Ÿ”—https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science

13. Mathematics
๐Ÿ”—https://matlabacademy.mathworks.com

14. Statistics
๐Ÿ”— https://lnkd.in/df6qksMB

15. Data Visualization
๐Ÿ”—https://imp.i384100.net/k0X6vx

16. Machine Learning
๐Ÿ”— https://imp.i384100.net/nLbkN9

17. Deep Learning
๐Ÿ”— https://imp.i384100.net/R5aPOR

18. Data Science: Linear Regression
๐Ÿ”—https://pll.harvard.edu/course/data-science-linear-regression/2023-10

19. Data Science: Wrangling
๐Ÿ”—https://edx.org/learn/data-science/harvard-university-data-science-wrangling

20. Linear Algebra
๐Ÿ”— https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

21. Probability
๐Ÿ”— https://pll.harvard.edu/course/data-science-probability

22. Introduction to Linear Models and Matrix Algebra
๐Ÿ”—https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra

23. Data Science: Capstone
๐Ÿ”— https://edx.org/learn/data-science/harvard-university-data-science-capstone

24. Data Analysis
๐Ÿ”— 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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Data Science Machine Learning Data Analysis
Forwarded from Python | Machine Learning | Coding | R
MIT's "Machine Learning" lecture notes

PDF: https://introml.mit.edu/_static/spring24/LectureNotes/6_390_lecture_notes_spring24.pdf

#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

https://t.iss.one/CodeProgrammer โœˆ๏ธ
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Data Science Machine Learning Data Analysis
"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)

๐Ÿ“˜ Freely available at: https://web.stanford.edu/~boyd/vmls/

๐Ÿ“ฝ Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9

#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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Data Science Machine Learning Data Analysis
๐Ÿƒ Stem-Leaf Plot - An intelligent visualization!

It's a simple and effective way to visualize and compare datasets.

๐Ÿ“Š Imagine we have two datasets: Set 1 (7, 12, 14, 17, 19, 23, 25) and Set 2 (3, 11, 16, 18, 20, 21, 24). We'll use a stem-leaf plot to compare them.

๐ŸŒฟ First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).

๐Ÿ” By comparing the plots, we can see that Dataset 1 has higher values in the tens place, while Dataset 2 has a more uniform distribution.

๐ŸŽฏ Stem-leaf plots are great for small datasets and provide a clear picture of data distribution. The special thing about a stem-and-leaf diagram is that the original data can be read out of the graphical representation.


Give it a try next time you need to compare datasets!

โœ๐Ÿฝ Have you used stem-leaf plots before?

#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

https://t.iss.one/CodeProgrammer โœˆ๏ธ
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Data Science Machine Learning Data Analysis
Forwarded from Python | Machine Learning | Coding | R
โœ”๏ธ Awesome AI/ML Resources: Learn AI/ML for beginners with a roadmap and free resources.

๐Ÿ–ฅ  https://github.com/armankhondker/awesome-ai-ml-resources

#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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Data Science Machine Learning Data Analysis
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โœ… Free ML Books You Should Know!

1โƒฃ Dive into Deep Learning: https://d2l.ai

๐Ÿ”ข Deep Learning: https://deeplearningbook.org

๐Ÿ”ข Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/

๐Ÿ”ข Machine Learning Yearning: https://info.deeplearning.ai/machine-learning-yearning-book

๐Ÿ”ข Mathematics for Machine Learning: https://mml-book.github.io

๐Ÿ”ข An Introduction to Statistical Learning: https://statlearning.com

#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 ๐Ÿ–ฅ
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Data Science Machine Learning Data Analysis
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๐Ÿ—‚ 10 โ€œReal Data Science Portfolioโ€ Examples

๐Ÿ“ I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!๐Ÿ‘‡
1๏ธโƒฃ Ken Jee Portfolio | Data Scientist
โ–ถ๏ธ Field: Sports data analysis
๐Ÿ‘ค Link: Portfolio

2๏ธโƒฃ Yassine Alouini's Portfolio | Kegel Master
โ–ถ๏ธ Domain: Machine Learning and Kegel Competitions
๐Ÿ‘ค Link: Portfolio

3๏ธโƒฃ Tatman Portfolio | Data Scientist
โ–ถ๏ธ Domain: Natural Language Processing (NLP)
๐Ÿ‘ค Link: Portfolio

4๏ธโƒฃ Robinson Portfolio | Data Scientist
โ–ถ๏ธ Field: Statistical analysis and R programming
๐Ÿ‘ค Link: Portfolio

5๏ธโƒฃ Siraj Raval's Portfolio | AI Instructor
โ–ถ๏ธ Field: Machine Learning and Artificial Intelligence
๐Ÿ‘ค Link: Portfolio

6๏ธโƒฃ Julia Silge's Portfolio | Data Scientist
โ–ถ๏ธ Domain: Organized data and data visualization
๐Ÿ‘ค Link: Portfolio

7๏ธโƒฃ Mueller Portfolio | Developer Scikit-Learn
โ–ถ๏ธ Field: Machine learning and open source projects
๐Ÿ‘ค Link: Portfolio

8๏ธโƒฃ Wickham Portfolio | Data Scientist
โ–ถ๏ธ Area: R programming and data visualization
๐Ÿ‘ค Link: Portfolio

9๏ธโƒฃ Portfolio of Franรงois Puget | Kegel Master
โ–ถ๏ธ Domain: Advanced Machine Learning Techniques
๐Ÿ‘ค Link: Portfolio

๐Ÿ”Ÿ Emily's Portfolio | Data Analyst at Disney
โ–ถ๏ธ Domain: Data visualization and storytelling
๐Ÿ‘ค Link: Portfolio

#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 ๐Ÿง 
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Data Science Machine Learning Data Analysis
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Supervised Learning: Classification and Regression

Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf

#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML


โœ‰๏ธ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk

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Data Science Machine Learning Data Analysis
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๐‹๐จ๐ ๐ข๐ฌ๐ญ๐ข๐œ ๐‘๐ž๐ ๐ซ๐ž๐ฌ๐ฌ๐ข๐จ๐ง ๐„๐ฑ๐ฉ๐ฅ๐š๐ข๐ง๐ž๐ ๐ฌ๐ข๐ฆ๐ฉ๐ฅ๐ฒ

If youโ€™ve just started learning Machine Learning, ๐‹๐จ๐ ๐ข๐ฌ๐ญ๐ข๐œ ๐‘๐ž๐ ๐ซ๐ž๐ฌ๐ฌ๐ข๐จ๐ง is one of the most important and misunderstood algorithms.

Hereโ€™s everything you need to know ๐Ÿ‘‡

๐Ÿ โ‡จ ๐–๐ก๐š๐ญ ๐ข๐ฌ ๐‹๐จ๐ ๐ข๐ฌ๐ญ๐ข๐œ ๐‘๐ž๐ ๐ซ๐ž๐ฌ๐ฌ๐ข๐จ๐ง?

Itโ€™s a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).

๐Ÿ โ‡จ ๐‡๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ?

It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a ๐ฌ๐ข๐ ๐ฆ๐จ๐ข๐ ๐Ÿ๐ฎ๐ง๐œ๐ญ๐ข๐จ๐ง to map the result between 0 and 1.

๐˜—๐˜ณ๐˜ฐ๐˜ฃ๐˜ข๐˜ฃ๐˜ช๐˜ญ๐˜ช๐˜ต๐˜บ = ๐Ÿ / (๐Ÿ + ๐žโป(๐ฐ๐ฑ + ๐›))

Here,
๐ฐ = weights
๐ฑ = inputs
๐› = bias
๐ž = Eulerโ€™s number (approx. 2.718)

๐Ÿ‘ โ‡จ ๐–๐ก๐ฒ ๐ง๐จ๐ญ ๐‹๐ข๐ง๐ž๐š๐ซ ๐‘๐ž๐ ๐ซ๐ž๐ฌ๐ฌ๐ข๐จ๐ง?

Because Linear Regression predicts any number from -โˆž to +โˆž, which doesnโ€™t make sense for probability.
We need outputs between 0 and 1 and thatโ€™s where the sigmoid function helps.

๐Ÿ’ โ‡จ ๐‹๐จ๐ฌ๐ฌ ๐…๐ฎ๐ง๐œ๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐ž๐?

๐๐ข๐ง๐š๐ซ๐ฒ ๐‚๐ซ๐จ๐ฌ๐ฌ-๐„๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ

โ„’ = โˆ’(y log(p) + (1 โˆ’ y) log(1 โˆ’ p))
Where y is the actual value (0 or 1), and p is the predicted probability

๐Ÿ“ โ‡จ ๐€๐ฉ๐ฉ๐ฅ๐ข๐œ๐š๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ซ๐ž๐š๐ฅ ๐ฅ๐ข๐Ÿ๐ž:

๐„๐ฆ๐š๐ข๐ฅ ๐’๐ฉ๐š๐ฆ ๐ƒ๐ž๐ญ๐ž๐œ๐ญ๐ข๐จ๐ง
๐ƒ๐ข๐ฌ๐ž๐š๐ฌ๐ž ๐๐ซ๐ž๐๐ข๐œ๐ญ๐ข๐จ๐ง
๐‚๐ฎ๐ฌ๐ญ๐จ๐ฆ๐ž๐ซ ๐‚๐ก๐ฎ๐ซ๐ง ๐๐ซ๐ž๐๐ข๐œ๐ญ๐ข๐จ๐ง
๐‚๐ฅ๐ข๐œ๐ค-๐“๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐‘๐š๐ญ๐ž ๐๐ซ๐ž๐๐ข๐œ๐ญ๐ข๐จ๐ง
๐๐ข๐ง๐š๐ซ๐ฒ ๐ฌ๐ž๐ง๐ญ๐ข๐ฆ๐ž๐ง๐ญ ๐œ๐ฅ๐š๐ฌ๐ฌ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง

๐Ÿ” โ‡จ ๐•๐ฌ. ๐Ž๐ญ๐ก๐ž๐ซ ๐‚๐ฅ๐š๐ฌ๐ฌ๐ข๐Ÿ๐ข๐ž๐ซ๐ฌ

Itโ€™s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.

๐Ÿ• โ‡จ ๐‚๐š๐ง ๐ข๐ญ ๐ก๐š๐ง๐๐ฅ๐ž ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ž ๐œ๐ฅ๐š๐ฌ๐ฌ๐ž๐ฌ?

Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.

๐Ÿ– โ‡จ ๐„๐ฑ๐š๐ฆ๐ฉ๐ฅ๐ž ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)


#LogisticRegression #MachineLearning #MLAlgorithms #SupervisedLearning #BinaryClassification #SigmoidFunction #PythonML #ScikitLearn #MLForBeginners #DataScienceBasics #MLExplained #ClassificationModels #AIApplications #PredictiveModeling #MLRoadmap

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