Preparing for a data science interview can be challenging, but with the right approach, you can increase your chances of success. Here are some tips to help you prepare for your next data science interview:

👉 1. Review the Fundamentals: Make sure you have a thorough understanding of the fundamentals of statistics, probability, and linear algebra. You should also be familiar with data structures, algorithms, and programming languages like Python, R, and SQL.

👉 2. Brush up on Machine Learning: Machine learning is a key aspect of data science. Make sure you have a solid understanding of different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.

👉 3. Practice Coding: Practice coding questions related to data structures, algorithms, and data science problems. You can use online resources like HackerRank, LeetCode, and Kaggle to practice.

👉 4. Build a Portfolio: Create a portfolio of projects that demonstrate your data science skills. This can include data cleaning, data wrangling, exploratory data analysis, and machine learning projects.

👉 5. Practice Communication: Data scientists are expected to effectively communicate complex technical concepts to non-technical stakeholders. Practice explaining your projects and technical concepts in simple terms.

👉 6. Research the Company: Research the company you are interviewing with and their industry. Understand how they use data and what data science problems they are trying to solve.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

How to Master Networking

Looking to expand your connections? Use these tips!

1. Be genuine and approachable in conversations.
2. Prepare a brief, engaging introduction about yourself.
3. Follow up with new contacts to build lasting relationships.
4. Offer help and value to others without expecting immediate returns.
5. Attend industry events and stay active on professional platforms.

List of top 10 hard skills:

1. Cloud Computing
2. Data Analysis
3. Digital Marketing
4. Cybersecurity
5. Artificial Intelligence (AI) and Machine Learning (ML)
6. Web Development
7. Database Management
8. Networking
9. Software Development
10. Graphic Design

Keyboard shortcuts for Telegram Desktop ⌨️

Action : Command

• Move to next chat : Ctrl + Tab
• Move to next chat : Ctrl + PageDown
• Move to next chat : Alt + Arrow Down
• Move to previous chat : Ctrl + Shift + Tab
• Move to previous chat : Ctrl + PageUp
• Move to previous chat : Alt + Arrow Up
• Go to Previous Folder : Ctrl + Shift + Arrow Up
• Go to Next Folder : Ctrl + Shift + Arrow Down
• Search selected chat : Ctrl + F
• Exit selected chat and search Telegram : Esc
• Exit display of current chat/channel   : Esc
• Delete currently selected message : Delete
• Quit Telegram : Ctrl + Q
• Lock Telegram (if Local Password is set) : Ctrl + L
• Iconify (Minimize) Telegram : Ctrl + M
• Iconify (Minimize) Telegram to System Tray : Ctrl + W
• Edit Previous Message : Arrow Up
• Start New Line in Input Area : Ctrl + Enter or Shift + Enter
• Move Cursor to Start of Multi-line Message : Ctrl + Home
• Make Text Italic : Ctrl + I
• Make Text Bold : Ctrl + B
• Make Text Underline : Ctrl + U
Make Text Striketrough : Ctrl + Shift + X
• Make Text Monospace : Ctrl + Shift + M
• Remove Text Formatting (Make Selection Plain Text) : Ctrl + Shift + N
• PH4N745M
• Add URL to Selected Text (Make Link) : Ctrl + K
• Send File : Ctrl + O
• Open Contacts : Ctrl + J
• Fast Scroll : Scroll with Ctrl or Shift pressed.
• Reply in another chat : Ctrl+Click on Reply in the menu.
• Jump to a message from the reply panel : Ctrl + LMB.
• Open conversation in a separate tab : Ctrl + click.
• Jump between Folders :  Ctrl + 1,2,3...


#Desktop #Shortcuts #Tips

🧠 Build your own ChatGPT

Build an LLM app with Mixture of AI Agents using small Open Source LLMs that can beat GPT-4o in just 40 lines of Python Code

⬇️ step-by-step instructions ⬇️

1. Install the necessary Python Libraries

Run the following commands from your terminal to install the required libraries:

2. Import necessary libraries

• Streamlit for the web interface
• asyncio for asynchronous operations
• Together AI for LLM interactions

3. Set up the Streamlit app and API key input.

• Creates a title for the app
• Adds a secure input field for the Together API key

4. Initialize Together AI clients.

• Sets up Together API key as an environment variable
• Initializes both synchronous and asynchronous Together clients

5. Define the models and aggregator system prompt.

• Specifies the LLMs to be used for generating responses
• Defines the aggregator model and its system prompt

6. Implement the LLM call function.

• Asynchronously calls the LLM with the user's prompt
• Returns the model name and its response

7. Define the main function to run all LLMs and aggregate results.

• Runs all reference models asynchronously
• Displays individual responses in expandable sections
• Aggregates responses using the aggregator model
• Streams the aggregated response.

8. Set up the user interface and trigger the main function.

• Provides an input field for the user's question
• Triggers the main function when the user clicks "Get Answer"

HIGH-INCOME SKILLS TO LEARN💰

1. Artificial Intelligence
2. Cloud Computing
3. Data Science
4. Machine Learning
5. Blockchain
6. Data Analytics
7. Data Engineering
8. Applications Engineering
9. Web Development
10. Software Development
11. UX Design
12. Web Design
13. Graphic Design
14. Video Editing
15. Content Marketing
16. Digital Marketing

OpenAI New Model-01

Don't let snake-oil salesmen fool you. This new model released by OpenAI today doesn't "think." It just generates an extensive "chain of thought", which is just a discussion of the model with itself that looks like a person talking to themselves.

Previous models were trained to be used in one-shot. You ask a question, you get an answer. Because of randomness in next token generation, if you were unlucky, your answer might be wrong. This model was finetuned to generate a long (and hidden from the user by the UI) discussion on how to better solve the problem, what facts are known, what assumptions need to be made, and what constraints should be respected.

If you explicitly asked previous models to generate this discussion before answering your question, you would get a better quality result, because the final answer would be conditioned on the information contained in this discussion.

They seemingly optimized their model to generate good quality discussions (without the user asking for it) by using reinforcement learning on various problems that have a verifiable solution, so that a reward for finding the right answer could be automatically assigned. For example:

Question: 1+1 = ?
Discussion: we have 1 and we have 1 more. And we have a plus sign, so it's an addition. What happens if we add 1 and 1? It means 1 is incremented by 1. When we increment 1 by 1, what do we get? Let's count: 1, 2, 3, 4,... Ok, 2 comes after 1, so 1 + 1 must be 2.
Answer: 2
Reward: 1

Question: 1+1 = ?
Discussion: It's easy. 1+1=11
Answer: 11
Reward: 0

Once the model is trained, what the user sees:

Question: 1+1 = ?
(Discussion happens behinds the scenes.)
Answer: 2

Sure! Here's a more polished version of the statement:

Don't get swept away by the hype around AI; Stay grounded and approach it thoughtfully. 💯

In my teen's I thought:

• A fancy job title.
• A huge salary.
• A demanding job.
= WINNING

In my 20s I realized:

• Having control of your time.
• Doing work you enjoy.
• Seeing family more happy.
= WINNING

Life Looks Different Every Decade.

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