𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗔𝗰𝗿𝗼𝘀𝘀 𝗜𝗻𝗱𝗶𝗮 | 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 😍
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Select your experience & Complete The Registration Process
Select the company name & apply for the role that matches you
Roles Hiring:- Tech & Non Tech Roles
Salary Range :- 5 To 24LPA
Qualification:- Graduate/Post Graduate
𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇:-
https://bit.ly/44qMX2k
Select your experience & Complete The Registration Process
Select the company name & apply for the role that matches you
❤1
9 tips to get better at debugging code:
Read error messages carefully — they often tell you everything
Use print/log statements to trace code execution
Check one small part at a time
Reproduce the bug consistently
Use a debugger to step through code line by line
Compare working vs broken code
Check for typos, null values, and off-by-one errors
Rubber duck debugging — explain your code out loud
Take breaks — fresh eyes spot bugs faster
Coding Interview Resources:👇 https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
Read error messages carefully — they often tell you everything
Use print/log statements to trace code execution
Check one small part at a time
Reproduce the bug consistently
Use a debugger to step through code line by line
Compare working vs broken code
Check for typos, null values, and off-by-one errors
Rubber duck debugging — explain your code out loud
Take breaks — fresh eyes spot bugs faster
Coding Interview Resources:👇 https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
ENJOY LEARNING 👍👍
❤1
🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍
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𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗧𝗼 𝗚𝗲𝘁 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
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Here are some essential SQL tips for beginners 👇👇
◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more 😄😄
◆ Primary Key = Unique Key + Not Null constraint
◆ To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE ‘A%A’
◆ LIKE operator is for string data type
◆ COUNT(*), COUNT(1), COUNT(0) all are same
◆ All aggregate functions ignore the NULL values
◆ Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
◆ For row level filtration use WHERE and aggregate level filtration use HAVING
◆ UNION ALL will include duplicates where as UNION excludes duplicates
◆ If the results will not have any duplicates, use UNION ALL instead of UNION
◆ We have to alias the subquery if we are using the columns in the outer select query
◆ Subqueries can be used as output with NOT IN condition.
◆ CTEs look better than subqueries. Performance wise both are same.
◆ When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
◆ Window functions work at ROW level.
◆ The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
◆ EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.
Like for more 😄😄
❤1
𝗪𝗮𝗻𝘁 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗧𝗵𝗮𝘁 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗔𝗿𝗲 𝗛𝗶𝗿𝗶𝗻𝗴 𝗙𝗼𝗿?😍
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Here’s your roadmap — pick one, stay consistent, and grow daily✅️
If you’re looking to land a job in tech or simply want to upskill without spending money, this is your golden chance✨️📌
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Here’s your roadmap — pick one, stay consistent, and grow daily✅️
❤3
Guide to Building an AI Agent
1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
📌 Choosing the right approach improves reasoning & reliability.
3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
📌 Clear system prompts shape agent behavior.
4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
📌 Example: A financial AI recalls risk tolerance from past chats.
5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Denoscription: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
📌 Example: A support AI retrieves order details via CRM API.
6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I don’t offer legal advice.")
📌 Example: A financial AI focuses on finance, not general knowledge.
7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
📌 Example: A financial AI converts extracted data into JSON.
8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
📌 Example:
1️⃣ One agent fetches data
2️⃣ Another summarizes
3️⃣ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
📌 Choosing the right approach improves reasoning & reliability.
3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
📌 Clear system prompts shape agent behavior.
4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
📌 Example: A financial AI recalls risk tolerance from past chats.
5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Denoscription: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
📌 Example: A support AI retrieves order details via CRM API.
6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I don’t offer legal advice.")
📌 Example: A financial AI focuses on finance, not general knowledge.
7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
📌 Example: A financial AI converts extracted data into JSON.
8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
📌 Example:
1️⃣ One agent fetches data
2️⃣ Another summarizes
3️⃣ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
❤1
Forwarded from Data Science & Machine Learning
𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!😍
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They’re real, portfolio-worthy projects you can start today✅️
Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑💻📌
Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻
𝐋𝐢𝐧𝐤👇:-
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They’re real, portfolio-worthy projects you can start today✅️
❤1
𝗚𝗲𝘁 𝗬𝗼𝘂𝗿 𝗗𝗿𝗲𝗮𝗺 𝗜𝗧 𝗝𝗼𝗯 𝗜𝗻 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍
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- Placement assistance
- 60+ hiring drives each month
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𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱:- https://pdlink.in/3EZpScU
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