๐ฅ2026 New IT Certification Prep Kit โ Free!
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๐ฌ Need exam help? Contact admin: wa.link/w6cems
โ Join our IT community: get free study materials, exam tips & peer support
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SPOTO cover: #Python #AI #Cisco #PMI #Fortinet #AWS #Azure #Excel #CompTIA #ITIL #Cloud + more
โ Grab yours free kit now:
โข Free Courses (Python, Excel, Cyber Security, Cisco, SQL, ITIL, PMP, AWS)
๐ https://bit.ly/3Ogtn3i
โข IT Certs E-book
๐ https://bit.ly/41KZlru
โข IT Exams Skill Test
๐ https://bit.ly/4ve6ZbC
โข Free AI Materials & Support Tools
๐ https://bit.ly/4vagTuw
โข Free Cloud Study Guide
๐ https://bit.ly/4c3BZCh
๐ฌ Need exam help? Contact admin: wa.link/w6cems
โ Join our IT community: get free study materials, exam tips & peer support
https://chat.whatsapp.com/BiazIVo5RxfKENBv10F444
โค1
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PhD Students โ How to write a systematic literature review draft in 1 day?
A systematic literature review takes 4-6 months.
You can reduce this time.
๐ฏ Here is how you can write it in 1 hour.
1๏ธโฃ Go to www.gatsbi.com
2๏ธโฃ Select Gatsbi reviewer from the drop-down menu
3๏ธโฃ Enter the topic of your literature review
4๏ธโฃ Gatsbi will generate an outline for review
5๏ธโฃ If you are OK with it, click on write manuscript.
6๏ธโฃ Gatsbi will write the literature review for you.
๐ The literature review contains the following parts
โ Title
โ Abstract
โ Introduction
โ Methodology
โ Results
โ Discussion
โ Conclusion
โ References
๐ This polished paper also contains
โ Diagrams
โ Tables
โ Equations
โ Graphs
Once the paper is ready, you can humanize the text.
Once humanized, you can download it in the following formats.
โณ MS Word
โณ Latex
โณ Markdown
After downloading, you can make any changes you want.
In addition to Gatsbi Reviewer, you can also use:
โ Gatsbi Innovator: Generate ideas before writing
โ Gatsbi Writer: Write research papers
๐ Try Gatsbi today for free: www.gatsbi.com
โ๏ธ Anything you'd like to add?
#phd #research #literature #review
A systematic literature review takes 4-6 months.
You can reduce this time.
๐ฏ Here is how you can write it in 1 hour.
1๏ธโฃ Go to www.gatsbi.com
2๏ธโฃ Select Gatsbi reviewer from the drop-down menu
3๏ธโฃ Enter the topic of your literature review
4๏ธโฃ Gatsbi will generate an outline for review
5๏ธโฃ If you are OK with it, click on write manuscript.
6๏ธโฃ Gatsbi will write the literature review for you.
๐ The literature review contains the following parts
โ Title
โ Abstract
โ Introduction
โ Methodology
โ Results
โ Discussion
โ Conclusion
โ References
๐ This polished paper also contains
โ Diagrams
โ Tables
โ Equations
โ Graphs
Once the paper is ready, you can humanize the text.
Once humanized, you can download it in the following formats.
โณ MS Word
โณ Latex
โณ Markdown
After downloading, you can make any changes you want.
In addition to Gatsbi Reviewer, you can also use:
โ Gatsbi Innovator: Generate ideas before writing
โ Gatsbi Writer: Write research papers
๐ Try Gatsbi today for free: www.gatsbi.com
โ๏ธ Anything you'd like to add?
#phd #research #literature #review
โค3
New and complimentary courses for PhD researchers in 2026 ๐
1. Understanding Research Methods ๐
โณ https://lnkd.in/g-xBFj4v
2. Research Design: Inquiry and Discovery ๐
โณ https://lnkd.in/dEkr-7tn
3. Quantitative Methods ๐
โณ https://lnkd.in/dGBf7EvP
4. Data Science: R Basics ๐ป
โณ https://lnkd.in/dFiRE2P7
5. Research Data Management and Sharing ๐
โณ https://lnkd.in/dNivuaYT
6. Introduction to Python ๐
โณ https://lnkd.in/dtWQXSWU
7. Writing in the Sciences โ๏ธ
โณ https://lnkd.in/dbEran3u
8. How to Write and Publish a Scientific Paper ๐
โณ https://lnkd.in/giwTe2is
9. Statistical Thinking and Data Analysis ๐
โณ https://lnkd.in/dMggEddB
10. AI for Scientific Research Specialization ๐ค
โณ https://lnkd.in/dmWXnrpB
11. Ensuring Visibility ๐
โณ https://lnkd.in/dTEU8dPM
12. Conference Skills for Researchers ๐ค
โณ https://lnkd.in/dwXkNmpZ
Are there any additional topics you would like to include?
#phd #research #course
1. Understanding Research Methods ๐
โณ https://lnkd.in/g-xBFj4v
2. Research Design: Inquiry and Discovery ๐
โณ https://lnkd.in/dEkr-7tn
3. Quantitative Methods ๐
โณ https://lnkd.in/dGBf7EvP
4. Data Science: R Basics ๐ป
โณ https://lnkd.in/dFiRE2P7
5. Research Data Management and Sharing ๐
โณ https://lnkd.in/dNivuaYT
6. Introduction to Python ๐
โณ https://lnkd.in/dtWQXSWU
7. Writing in the Sciences โ๏ธ
โณ https://lnkd.in/dbEran3u
8. How to Write and Publish a Scientific Paper ๐
โณ https://lnkd.in/giwTe2is
9. Statistical Thinking and Data Analysis ๐
โณ https://lnkd.in/dMggEddB
10. AI for Scientific Research Specialization ๐ค
โณ https://lnkd.in/dmWXnrpB
11. Ensuring Visibility ๐
โณ https://lnkd.in/dTEU8dPM
12. Conference Skills for Researchers ๐ค
โณ https://lnkd.in/dwXkNmpZ
Are there any additional topics you would like to include?
#phd #research #course
โค3
Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
โค1
We provide our services at competitive rates, backed by twenty years of experience. ๐
Please contact us via @Omidyzd62. ๐ฉ
Please contact us via @Omidyzd62. ๐ฉ
Telegram
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You can contact @Omidyzd62 right away.
โค2
๐ Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning
Both code and weights are available under the MIT license on HuggingFace.
๐ Key details:
โข Trained from scratch (not a finetune) on proprietary data and infrastructure
โข Mixture-of-Experts (MoE) architecture
Models:
๐ง GigaChat-3.1 Ultra
โข 702B MoE model for high-performance environments
โข Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
โข Supports FP8 training and MTP
โก๏ธ GigaChat-3.1 Lightning
โข 10B model (1.8B active parameters)
โข Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
โข Efficient local inference
โข Up to 256k context
Engineering highlights:
โข Custom metric to detect and reduce generation loops
โข DPO training moved to native FP8
โข Improvements in post-training pipeline
โข Identified and fixed a critical issue affecting evaluation quality
๐ Trained on 14 languages (optimized for English and Russian)
Use cases:
โข chatbots
โข AI assistants
โข copilots
โข internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
Both code and weights are available under the MIT license on HuggingFace.
๐ Key details:
โข Trained from scratch (not a finetune) on proprietary data and infrastructure
โข Mixture-of-Experts (MoE) architecture
Models:
๐ง GigaChat-3.1 Ultra
โข 702B MoE model for high-performance environments
โข Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
โข Supports FP8 training and MTP
โก๏ธ GigaChat-3.1 Lightning
โข 10B model (1.8B active parameters)
โข Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
โข Efficient local inference
โข Up to 256k context
Engineering highlights:
โข Custom metric to detect and reduce generation loops
โข DPO training moved to native FP8
โข Improvements in post-training pipeline
โข Identified and fixed a critical issue affecting evaluation quality
๐ Trained on 14 languages (optimized for English and Russian)
Use cases:
โข chatbots
โข AI assistants
โข copilots
โข internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
โค4
Forwarded from Machine Learning with Python
โ๏ธ 10 Books to Understand How Large Language Models Function (2026)
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. ๐๐ค
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. ๐๐ค
โค4
Forwarded from Machine Learning with Python
๐ 12 Essential Articles for Data Scientists
๐ท Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
๐ท Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
๐ท Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
๐ท Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
๐ท Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
๐ท Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
๐ท Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
๐ท Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
๐ท Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
๐ท Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
๐ท Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
๐ท Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.iss.one/CodeProgrammer๐
๐ท Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
๐ท Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
๐ท Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
๐ท Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
๐ท Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
๐ท Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
๐ท Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
๐ท Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
๐ท Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
๐ท Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
๐ท Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
๐ท Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.iss.one/CodeProgrammer
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PhD Students: How to humanize AI-generated text in seconds? ๐๐ค
Introducing Humanizethis, a complimentary tool designed to humanize your text. ๐
Here is the procedure:
1. Navigate to https://humanizethis.io ๐
2. Copy and paste your text or upload the relevant file ๐
3. HumanizeThis will process the text to humanize it within seconds โฑ๏ธ
4. Subsequently, the system verifies the content through eight AI detectors, including:
- TurnItIn
- GPTZero
- Originality AI
- CopyLeaks, and others
5. These detectors confirm that the text has been successfully humanized โ
6. Users may also review the tracked changes ๐
7. Approve the modifications and copy the humanized text ๐
Notably, this service is provided at no cost. ๐ฐ
#phd #research
Introducing Humanizethis, a complimentary tool designed to humanize your text. ๐
Here is the procedure:
1. Navigate to https://humanizethis.io ๐
2. Copy and paste your text or upload the relevant file ๐
3. HumanizeThis will process the text to humanize it within seconds โฑ๏ธ
4. Subsequently, the system verifies the content through eight AI detectors, including:
- TurnItIn
- GPTZero
- Originality AI
- CopyLeaks, and others
5. These detectors confirm that the text has been successfully humanized โ
6. Users may also review the tracked changes ๐
7. Approve the modifications and copy the humanized text ๐
Notably, this service is provided at no cost. ๐ฐ
#phd #research
โค2
Research Papers PHD
PhD Students: How to humanize AI-generated text in seconds? ๐๐ค Introducing Humanizethis, a complimentary tool designed to humanize your text. ๐ Here is the procedure: 1. Navigate to https://humanizethis.io ๐ 2. Copy and paste your text or upload the relevantโฆ
We provide our services at competitive rates, backed by twenty years of experience. ๐
Please contact us via @Omidyzd62. ๐ฉ
Please contact us via @Omidyzd62. ๐ฉ
โค2
Have you ever published a scientific research paper in a scientific journal?
Final Results
28%
Yes ๐
72%
No ๐
โค1
Annotated example of a strong research article.
Good papers donโt try to sound smart
They try to make the science clear
A research paper is a guided tour of your thinking
If readers get lost, the writing needs work
Clarity > complexity. Always.
#AcademicWriting #PhDLife #ResearchTips #ScientificWriting #WriteBetter
https://t.iss.one/DataScienceY๐
Good papers donโt try to sound smart
They try to make the science clear
A research paper is a guided tour of your thinking
If readers get lost, the writing needs work
Clarity > complexity. Always.
#AcademicWriting #PhDLife #ResearchTips #ScientificWriting #WriteBetter
https://t.iss.one/DataScienceY
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โค5๐1
Today, the public mint for Lobsters on TON goes live on Getgems ๐ฆ
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint โ it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from โjust an imageโ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
๐ Mint your Lobster on Getgems ๐ฆ๐ฆ๐ฆ
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint โ it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from โjust an imageโ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
๐ Mint your Lobster on Getgems ๐ฆ๐ฆ๐ฆ
โค2
Searched 35 free courses, so you don't have to! ๐โจ
Here are the 35 best free courses: ๐
1. Data Science: Machine Learning ๐ค
Link: https://lnkd.in/gUNVYgGB
2. Introduction to computer science ๐ป
Link: https://lnkd.in/gR66-htH
3. Introduction to programming with scratch ๐งฉ
Link: https://lnkd.in/gBDUf_Wx
4. Computer science for business professionals ๐ผ
Link: https://lnkd.in/g8gQ6N-H
5. How to conduct and write a literature review ๐
Link: https://lnkd.in/gsh63GET
6. Software Construction ๐
Link: https://lnkd.in/ghtwpNFJ
7. Machine Learning with Python: from linear models to deep learning ๐๐ง
Link: https://lnkd.in/g_T7tAdm
8. Startup Success: How to launch a technology company in 6 steps ๐
Link: https://lnkd.in/gN3-_Utz
9. Data analysis: statistical modeling and computation in applications ๐
Link: https://lnkd.in/gCeihcZN
10. The art and science of searching in systematic reviews ๐
Link: https://lnkd.in/giFW5q4y
11. Introduction to conducting systematic review ๐
Link: https://lnkd.in/g6EEgCkW
12. Introduction to computer science and programming using python ๐ฅ
Link: https://lnkd.in/gwhMpWck
13. Introduction to computational thinking and data science ๐ก
Link: https://lnkd.in/gfjuDp5y
14. Becoming an Entrepreneur ๐ธ
Link: https://lnkd.in/gqkYmVAW
15. High-dimensional data analysis ๐
Link: https://lnkd.in/gv9RV9Zc
16. Statistics and R ๐
Link: https://lnkd.in/gUY3jd8v
17. Conduct a literature review ๐
Link: https://lnkd.in/g4au3w2j
18. Systematic Literature Review: An Introduction ๐ง
Link: https://lnkd.in/gVwGAzzY
19. Introduction to systematic review and meta-analysis ๐งฎ
Link: https://lnkd.in/gnpN9ivf
20. Creating a systematic literature review โ๏ธ
Link: https://lnkd.in/gbevCuy6
21. Systematic reviews and meta-analysis ๐
Link: https://lnkd.in/ggnNeX5j
22. Research methodologies ๐ต๏ธโโ๏ธ
Link: https://lnkd.in/gqh3VKCC
23. Quantitative and Qualitative research for beginners ๐๐ฌ
Link: https://shorturl.at/uNT58
24. Writing case studies: science of delivery ๐
Link: https://shorturl.at/ejnMY
25. research methodology: complete research project blueprint ๐บ
Link: https://lnkd.in/gFU8Nbrv
26. How to write a successful research paper ๐
Link: https://lnkd.in/g-ni3u5q
27. Research proposal bootcamp: how to write a research proposal ๐โโ๏ธ
Link: https://lnkd.in/gNRitBwX
28. Understanding technology ๐ฑ
Link: https://lnkd.in/gfjUnHfd
29. Introduction to artificial intelligence with Python ๐ค๐
Link: https://lnkd.in/gygaeAcY
30. Introduction to programming with Python ๐ป
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