Microsoft Power BI For Dummies.pdf
25.9 MB
Microsoft Power BI For Dummies PDF
Expert_Data_Modeling_with_Power_BI_Get_the_best.epub
62.4 MB
Expert Data Modeling with Power BI
Soheil Bakhshi, 2021
Soheil Bakhshi, 2021
Learning_Microsoft_Power_Bi_Transforming_Data_Into.epub
15.9 MB
Learning Microsoft Power Bi
Jeremey Arnold, 2023
Jeremey Arnold, 2023
Expert_Data_Modeling___Power_BI.pdf
47.5 MB
Expert Data Modeling with Power BI
Soheil Bakhshi, 2023
Soheil Bakhshi, 2023
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vdoc.pub_data-visualization-a-practical-introduction.pdf
12.2 MB
Data Visualization
Kieran Healy, 2019
Kieran Healy, 2019
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📌 Practice your coding skills here 👇
1. LeetCode
2. HackerRank
3. CodeSignal
4. CodeChef
5. TopCoder
6. Frontend Mentor
7. freeCodeCamp
8. CodePen
9. GeeksforGeeks
10. W3Schools
11. Scrimba
12. Coderbyte
13. Project Euler
14. SoloLearn
15. Codewars
16. DevChallenges
17. The Odin Project
18. Practice. dev
19. Pluralsight
20. CodeCombat
21. AlgoExpert
22. Programiz
23. Hack The Box
24. Edabit
25. Exercism
1. LeetCode
2. HackerRank
3. CodeSignal
4. CodeChef
5. TopCoder
6. Frontend Mentor
7. freeCodeCamp
8. CodePen
9. GeeksforGeeks
10. W3Schools
11. Scrimba
12. Coderbyte
13. Project Euler
14. SoloLearn
15. Codewars
16. DevChallenges
17. The Odin Project
18. Practice. dev
19. Pluralsight
20. CodeCombat
21. AlgoExpert
22. Programiz
23. Hack The Box
24. Edabit
25. Exercism
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Data analyst vs data scientist:
- Data analysts analyse what has happened
- Data scientists try to predict what will happen
- Both use similar tools, but their focus differs.
Visualisation is key for both, but more so for DAs as DS lean towards model building.
- Data analysts analyse what has happened
- Data scientists try to predict what will happen
- Both use similar tools, but their focus differs.
Visualisation is key for both, but more so for DAs as DS lean towards model building.
deep learning notes.pdf
19.1 MB
Deep Learning Notes
Data_Science_from_Scratch_First_Principles_with_Python_by_Joel_Grus.pdf
10.8 MB
Data Science from Scratch First Principles with Python by Joel Grus z lib
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Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_data
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/datasciencej
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/aiindi
Data Analysts: https://t.iss.one/sqlspecialist
Hope it helps :)
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.iss.one/sqlanalyst
Power BI & Tableau: https://t.iss.one/PowerBI_analyst
Excel: https://t.iss.one/excel_data
Python: https://t.iss.one/dsabooks
Jobs: https://t.iss.one/datasciencej
Data Science: https://t.iss.one/datasciencefree
Artificial intelligence: https://t.iss.one/aiindi
Data Analysts: https://t.iss.one/sqlspecialist
Hope it helps :)
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Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
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