Forwarded from Python | Machine Learning | Coding | R
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
🔗http://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
🔗http://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 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
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
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"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)
📘 Freely available at: https://web.stanford.edu/~boyd/vmls/
📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9
📘 Freely available at: https://web.stanford.edu/~boyd/vmls/
📽 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
https://yangx.top/CodeProgrammer✈️
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🍃 Stem-Leaf Plot - An intelligent visualization!
It's a simple and effective way to visualize and compare datasets.
Give it a try next time you need to compare datasets!
✍🏽 Have you used stem-leaf plots before?
It's a simple and effective way to visualize and compare datasets.
📊 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.
🌿 First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).
🔍 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.
🎯 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!
✍🏽 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://yangx.top/CodeProgrammer ✈️
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Forwarded from Python | Machine Learning | Coding | R
#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://yangx.top/CodeProgrammer🖥
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#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://yangx.top/CodeProgrammer🖥
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Forwarded from Python | Machine Learning | Coding | R
📁 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!👇
#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://yangx.top/CodeProgrammer🧠
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Forwarded from Python | Machine Learning | Coding | R
Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐬𝐢𝐦𝐩𝐥𝐲
If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.
Here’s everything you need to know 👇
𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
It’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).
𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.
𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))
Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)
𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.
𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?
𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲
ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability
𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:
𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬
It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧
If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.
Here’s everything you need to know 👇
𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
It’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).
𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.
𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))
Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)
𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?
Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.
𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?
𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲
ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability
𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:
𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧
𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬
It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧
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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