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7 Websites to Learn Data Science for FREE🧑💻
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
✅ w3school
✅ datasimplifier
✅ hackerrank
✅ kaggle
✅ geeksforgeeks
✅ leetcode
✅ freecodecamp
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Free courses to learn Data Science in 2023
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Essential Data Science Skills for data scientists
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https://www.linkedin.com/posts/sql-analysts_datascience-dataskills-transferableskills-activity-7128372157230809089-GHRF
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ChatGPT for Data Scientist
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https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
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https://www.linkedin.com/posts/sql-analysts_chatgpt-for-data-science-activity-7128583314378043393-kYzL
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Data Science & Machine Learning
Do you want Harvard Resume and CV Career Guide?
Nice to 200+ people interested in this
Here is a free resume guide from Harvard
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👍4
Which social media platform do you use the most?
Anonymous Poll
54%
Telegram
40%
Whatsapp
26%
Instagram
10%
Twitter
32%
YouTube
3%
Tiktok
18%
Linkedin
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🤬6😁5👍3🥰3
Which of the following python library is used for data visualization?
Anonymous Quiz
92%
Matplotlib
8%
Numpy
👍8😢4
365 data science courses for free till Nov 20
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https://www.linkedin.com/posts/sql-analysts_datascience-dataanalytics-activity-7128217526924177408-0DtE
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Top 20 Pandas Interview Questions with Answers
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https://datasimplifier.com/pandas-interview-questions-with-answers/
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https://datasimplifier.com/pandas-interview-questions-with-answers/
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Which function is used to read CSV file in Pandas?
Anonymous Quiz
7%
read_file()
86%
read_csv()
5%
readcsvfile()
3%
readfile()
😁7👎1
Forwarded from Data Science Projects
Python Science Projects.pdf_20231120_013618_0000.pdf
2.1 MB
Python Data Science Projects For Boosting Your Portfolio
👍8❤1👏1
10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
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