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#CTF writeup database
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Free Certification Courses to Learn Data Analytics in 2025:
1. Python
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2. SQL
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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
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8. R
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9. Tableau
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10. PowerBI
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13. Mathematics
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14. Statistics
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18. Data Science: Linear Regression
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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
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26. Neural Networks and Deep Learning
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27. Supervised Machine Learning: Regression and Classification
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How to run 🐋 DeepSeek locally on your Computer
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ML, DL, AND AI Cheat Sheet.pdf7.46 MB
Looking to level up your knowledge in Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI)?
Check out this comprehensive cheat sheet compiled by experts from Stanford University and MIT! It covers:
✅ Probability & Statistics – The backbone of ML & AI
✅ Supervised Learning – Linear regression, logistic regression, SVMs, and more
✅ Unsupervised Learning – Clustering, PCA, ICA, and dimensionality reduction
✅ Deep Learning – Neural networks, CNNs, RNNs, reinforcement learning
✅ Mathematical Foundations – Linear algebra, calculus, optimization
✅ ML Tips & Tricks – Model selection, performance metrics, and debugging
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A must-have for anyone diving into AI, whether you're a beginner or a pro!
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Pi Coin Selling Price again Increased Now Current Price 40 Rupees
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Mistakes to Avoid as a JavaScript Developer.pdf.pdf23.12 MB
⚠️ O'Reilly Media, one of the most reputable publishers in the fields of programming, data mining, and AI, has made 10 data science books available to those interested in this field for free .
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0⃣ Python Data Science Handbook
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1⃣ Python for Data Analysis book
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🔢 Fundamentals of Data Visualization book
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🔢 R for Data Science book
┌ Online
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🔢 Hands-On Data Visualization Book
┌ Online
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💡 A complete package for success in data science and machine learning interviews!
👩🏻💻 I found a GitHub repo full of resources you need to succeed in Data Science and Machine Learning interviews!
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1⃣ Practical cheat sheets: Important tips gathered in one place.
🔢 Cool books: resources worth your time!
🔢 Frequently Asked Interview Questions: Topics that are asked in most interviews and that you are likely to encounter.
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🔗 Here is the link: 👇
🔗 Cracking the data science interview
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Top 10 Machine Learning Algorithms
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+7
🔰 PWA:(Progressive Web Apps): The Complete Guide
These days, everything is made possible with the help of mobile phones and applications. For everything we have app, either it's food order, booking for a cab, flight or we can say every business has an app. It's true that users are spending most of their time in native apps instead of web. Re-engagement features keep users in native apps, Push notification brings users back even when the app is closed, and home-screen icons maintain visibility.
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬
Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.
𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.
Structure of JWT (JSON Web Token)
👩💻 A simple explanation of working with list in Python!
📢 Resource Alert: UCI Machine Learning Repository
If you're looking for datasets to practice and experiment with machine learning, check out the UCI Machine Learning Repository!
It's a long-standing resource, widely used by students, educators, and researchers to access a variety of datasets for ML projects.
Explore it here: https://archive.ics.uci.edu/datasets
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#MachineLearning #DataScience #AI #Resources
Answer: 8
since ** is the exponentiation operator and it is like we have 2 * 2 * 2
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What will be the output of the following JavaScript code?
