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Working early with a sharp mind is like coding on a MacBook Pro, fast, efficient, and smooth. Working late and tired is like debugging on a dual-core i3, every thought lags and even simple tasks crash your system.

🔆 t.me/techpsyche

Ask Questions That Matter!

At the end of an interview, most candidates stick to the standard question, “What are the next steps?”

While this is perfectly fine, it’s a golden opportunity to stand out by asking more thoughtful questions.

📌Here are some alternative questions to consider:

1. Can you describe the team I would be working with?
2. What are the key challenges the team is currently facing?
3. How does this role contribute to the company's overall goals?
4. What opportunities for growth and development does the company offer?

By asking thoughtful questions, you not only gain valuable information but also leave a lasting impression on your interviewers.

So, take a moment to think beyond the basics, your curiosity could set you apart as the ideal candidate!

Remember, interviews are a two-way street!

Sample email template to reach out to HR’s as fresher 👇
t.me/jobsourceglobal/204

More Interview Tips Here:
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!!

Famous Programming Languages and Their Frameworks

1.Python:
Frameworks:
* Django
* Flask
* Pyramid
* Tornado

2.JavaScript:
Frameworks (Front-End):
* React
* Angular
* Vue.js
* Ember.js
_and thousands more...._

Frameworks (Back-End):
* Node.js (Runtime)
* Express.js
* Nest.js
* Meteor

3..Java:
Frameworks:
* Spring Framework
* Hibernate
* Apache Struts
* Play Framework

4.Ruby:
Frameworks:
* Ruby on Rails (Rails)
* Sinatra
* Hanami

5.PHP:
Frameworks:
* Laravel
* Symfony
* CodeIgniter
* Yii
* Zend Framework

6.C#:
Frameworks:
* .NET Framework
* ASP. NET
* ASP. NET Core

7.Go (Golang):
Frameworks:
* Gin
* Echo
* Revel

8.Rust:
Frameworks:
* Rocket
* Actix
* Warp

9.Swift:
Frameworks (iOS/macOS):
* SwiftUI
* UIKit
* Cocoa Touch

10.Kotlin:
Frameworks (Android):
* Android Jetpack
* Ktor

11.TypeScript:
Frameworks (Front-End):
* Angular
* Vue.js (with TypeScript)
* React (with TypeScript)

12.Scala:
Frameworks:
* Play Framework
* Akka

13.Perl:
Frameworks:
* Dancer
* Catalyst

14.Lua:
Frameworks:
* OpenResty (for web development)

15.Dart:
Frameworks:
* Flutter (for mobile app development)

16.R:
Frameworks (for data science and statistics):
* Shiny
* ggplot2

17.Julia:
Frameworks (for scientific computing):
* Pluto.jl
* Genie.jl

18.MATLAB:
Frameworks (for scientific and engineering applications):
* Simulink

19.COBOL:
Frameworks:
* COBOL-IT

20.Erlang:
Frameworks:
* Phoenix (for web applications)

21.Groovy:
Frameworks:
* Grails (for web applications)

Best Programming Resources: topmate.io/learning_resources/1362011

40 Windows Command Prompt commands: t.me/techpsyche/1946

ENJOY LEARNING 👍👍

Follow this WhatsApp Channel for More Resources
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

Don’t chase perfection—chase progress. Shipping code, asking questions, learning from mistakes, and improving every day will take you further than waiting until you “know enough.”
Focus on consistency, not just brilliance. That’s how great developers are made

🔆 t.me/techpsyche

Anthropic has released Claude Opus 5.5, its latest AI model, with a 1 million-token memory and the ability to produce up to 128,000 tokens in one response.

Anthropic says it is about 40% cheaper to run than the previous Opus 5 and is faster at tasks such as coding.

🔆 t.me/techpsyche

Microsoft 100% Free Courses: AI, Cybersecurity Azure & More

- Azure
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6 Tech Courses From Michigan University 👇👇

1. Intro to AR/VR/MR/XR: Technologies, Applications & Issues
https://www.coursera.org/learn/intro-augmented-virtual-mixed-extended-reality-technologies-applications-issues

2. JavaScript, jQuery, and JSON
https://www.coursera.org/learn/javascript-jquery-json

3. Introduction to User Experience Principles and Processes
https://www.coursera.org/learn/introtoux-principles-and-processes

4. Web Programming with Python and JavaScript
https://intercomuniversity.com/free-courses/artificial-intelligence/harvards-4-free-courses-to-catapult-your-tech-career/

5. Data Collection and Processing with Python
https://www.coursera.org/learn/data-collection-processing-python

6. Building Web Applications in Django
https://www.coursera.org/learn/django-build-web-apps

Free Full Stack Certifications Courses
https://t.me/freecoursescentre/157

ENJOY LEARNING 👍👍

Join https://t.me/freecoursescentre for more:

More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

💬 A Small Act, a Big Impact

🔰 Hacking manuals:

▪️ https://hackproofhacks.com
▪️ https://www.offensive-security.com
▪️ https://t.me/zerotrusthackers
▪️ https://www.youtube.com/user/Hak5Darren
▪️ https://www.youtube.com/user/sansinstitute
▪️ https://vimeo.com/channels/fullscopesecurity
▪️ http://www.kalitutorials.net/2013/08/kali-linux.html
▪️ https://www.youtube.com/user/DEFCONConference
▪️ https://en.wikibooks.org/wiki/Metasploit/VideoTutorials

🔰 Antiviruses:
▪️ http://virusscan.jotti.org/it
▪️ https://www.virustotal.com/nl/

🔰 Services for working with IP:
▪️ http://ip-api.com/
▪️ http://ipaddress.com
▪️ http://whatstheirip.com
▪️ http://www.whatismyip.com/
▪️ https://t.me/zerotrusthackers
▪️ http://www.ip2location.com/demo
▪️ http://www.my-ip-neighbors.com/
▪️ http://freegeoip.net/static/index.html
▪️ http://www.ip-adress.com/ipaddresstolocation/

Useful Wireshark Filters: https://t.me/zerotrusthackers/232

Awesome Security Repositories: https://t.me/zerotrusthackers/234

➡️ Give 100+ Reactions 🤟

Security WhatsApp Channel
https://whatsapp.com/channel/0029VaxVv551iUxRku094918

There has never been a better time to become a data analyst.

Tackle the tools:

- Excel
- SQL
- PowerBI/Tableau
- Python/R

Sharpen these soft skills:

- Communication
- Storytelling
- Critical thinking
- Business acumen

And let your journey begin.

Tableau Learning plan: t.me/dataanalysisresourcestp/229

Breaking into Data Analysis: t.me/dataanalysisresourcestp/223

Like this post if you want me to continue posting more 👍♥️

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Free Coding Courses

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Few more courses ✅

𝟭.𝗗𝗮𝘁𝗮 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-non-relational-data/

https://t.me/datascienceresourcestp

𝟮.𝗦𝗾𝗹 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://t.me/sqlresourcestp

https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/

𝟯.𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
https://learn.microsoft.com/en-us/training/paths/create-use-analvtics-reports-power-bi/

𝟰.𝗔𝘇𝘂𝗿𝗲 𝗰𝗼𝘀𝗺𝗼𝘀 𝗗𝗕
https://learn.microsoft.com/en-us/training/paths/create-use-analytics-reports-power-bi/

𝟱.𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://t.me/airesourcestp

https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/

WhatsApp Channel:
https://whatsapp.com/channel/0029Vatt3A635fLoJKXrp71G

How to Choose Charts for Your Data

A-Z of Essential Data Science Concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

Best Data Science & Machine Learning Resources: topmate.io/learning_resources/1406977

Credits: t.me/datascienceresourcestp

Like if you need similar content 😄👍

Hope this helps you 😊

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Data Science Projects based on domain

ChatGPT Cheat Sheet

AI Product Management

AI Product Management is evolving rapidly. The growth of generative AI and AI-based developer tools has created numerous opportunities to build AI applications. This is making it possible to build new kinds of things, which in turn is driving shifts in best practices in product management — the discipline of defining what to build to serve users — because what is possible to build has shifted. In this post, I’ll share some best practices I have noticed.

(Note: This is quite a lengthy post, but it is very informative)

Use concrete examples to specify AI products. Starting with a concrete idea helps teams gain speed. If a product manager (PM) proposes to build “a chatbot to answer banking inquiries that relate to user accounts,” this is a vague specification that leaves much to the imagination. For instance, should the chatbot answer questions only about account balances or also about interest rates, processes for initiating a wire transfer, and so on? But if the PM writes out a number (say, between 10 and 50) of concrete examples of conversations they’d like a chatbot to execute, the scope of their proposal becomes much clearer. Just as a machine learning algorithm needs training examples to learn from, an AI product development team needs concrete examples of what we want an AI system to do. In other words, the data is your PRD (product requirements document)!

In a similar vein, if someone requests “a vision system to detect pedestrians outside our store,” it’s hard for a developer to understand the boundary conditions. Is the system expected to work at night? What is the range of permissible camera angles? Is it expected to detect pedestrians who appear in the image even though they’re 100m away? But if the PM collects a handful of pictures and annotates them with the desired output, the meaning of “detect pedestrians” becomes concrete. An engineer can assess if the specification is technically feasible and if so, build toward it. Initially, the data might be obtained via a one-off, scrappy process, such as the PM walking around taking pictures and annotating them. Eventually, the data mix will shift to real-word data collected by a system running in production.

Using examples (such as inputs and desired outputs) to specify a product has been helpful for many years, but the explosion of possible AI applications is creating a need for more product managers to learn this practice.

Assess technical feasibility of LLM-based applications by prompting. When a PM scopes out a potential AI application, whether the application can actually be built — that is, its technical feasibility — is a key criterion in deciding what to do next. For many ideas for LLM-based applications, it’s increasingly possible for a PM, who might not be a software engineer, to try prompting — or write just small amounts of code — to get an initial sense of feasibility.

For example, a PM may envision a new internal tool for routing emails from customers to the right department (such as customer service, sales, etc.). They can prompt an LLM to see if they can get it to select the right department based on an input email, and see if they can achieve high accuracy. If so, this gives engineering a great starting point from which to implement the tool. If not, the PM can falsify the idea themselves and perhaps improve the product idea much faster than if they had to rely on an engineer to build a prototype.

Often, testing feasibility requires a little more than prompting. For example, perhaps the LLM-based email system needs basic RAG capability to help it make decisions. Fortunately, the barrier to writing small amounts of code is now quite low, since AI can help by acting as a coding companion, as I describe in the course, “AI Python for Beginners.” This means that PMs can do much more technical feasibility testing, at least at a basic level, than was possible before.

Continuation Here...

Top 10 AI Courses

Cybersecurity Roadmap
|
|-- Fundamentals
| |-- Introduction to Cybersecurity
| | |-- Importance and Principles of Cybersecurity
| | |-- Types of Cybersecurity (Network, Information, Application, Cloud, etc.)
| | |-- Cybersecurity Threat Landscape (Malware, Phishing, Ransomware, etc.)
| |-- Network Security
| | |-- Firewalls and VPNs
| | |-- Intrusion Detection Systems (IDS)
| | |-- Intrusion Prevention Systems (IPS)
| | |-- Network Access Control
|
|-- Threats and Vulnerabilities
| |-- Types of Cyber Threats
| | |-- Malware (Viruses, Worms, Trojans, etc.)
| | |-- Phishing and Social Engineering
| | |-- Denial of Service (DoS) Attacks
| | |-- Insider Threats
| |-- Vulnerability Assessment
| | |-- Vulnerability Scanning
| | |-- Penetration Testing (Ethical Hacking)
| | |-- Security Audits and Assessments
|
|-- Encryption and Cryptography
| |-- Introduction to Cryptography
| | |-- Symmetric and Asymmetric Encryption
| | |-- Hashing Algorithms (SHA, MD5, etc.)
| | |-- Public Key Infrastructure (PKI)
| |-- Encryption Protocols
| | |-- SSL/TLS
| | |-- IPsec
|
|-- Identity and Access Management (IAM)
| |-- Authentication Mechanisms
| | |-- Password Policies and Multi-Factor Authentication (MFA)
| | |-- Biometric Authentication
| |-- Access Control Models
| | |-- Role-Based Access Control (RBAC)
| | |-- Attribute-Based Access Control (ABAC)
| | |-- Mandatory Access Control (MAC)
|
|-- Incident Response and Forensics
| |-- Incident Response Process
| | |-- Detection, Containment, Eradication, Recovery
| | |-- Incident Response Teams (CSIRT)
| |-- Digital Forensics
| | |-- Evidence Collection and Preservation
| | |-- Data Recovery
| | |-- Forensic Tools (Autopsy, EnCase, etc.)
|
|-- Security Operations
| |-- Security Monitoring
| | |-- Security Information and Event Management (SIEM)
| | |-- Log Management and Analysis
| | |-- Threat Intelligence
| |-- Security Operations Center (SOC)
| | |-- SOC Roles and Responsibilities
| | |-- Incident Management
|
|-- Cloud Security
| |-- Cloud Security Principles
| | |-- Shared Responsibility Model
| | |-- Data Protection in Cloud Environments
| |-- Cloud Security Tools
| | |-- Cloud Access Security Brokers (CASB)
| | |-- Security in Cloud Platforms (AWS, Azure, Google Cloud)
|
|-- Application Security
| |-- Secure Software Development
| | |-- Secure Coding Practices
| | |-- Software Development Life Cycle (SDLC)
| | |-- Secure Code Reviews
| |-- Web Application Security
| | |-- OWASP Top 10
| | |-- SQL Injection, Cross-Site Scripting (XSS), CSRF
|
|-- Compliance and Regulations
| |-- Cybersecurity Standards
| | |-- ISO/IEC 27001, NIST Cybersecurity Framework
| | |-- CIS Controls, SOC 2
| |-- Data Privacy Regulations
| | |-- GDPR
| | |-- HIPAA, CCPA, PCI DSS
|
|-- Advanced Topics
| |-- Advanced Persistent Threats (APT)
| | |-- Detection and Mitigation
| | |-- Threat Hunting
| |-- Blockchain Security
| | |-- Cryptographic Principles
| | |-- Smart Contracts and Security
| |-- IoT Security
| | |-- Securing IoT Devices
| | |-- Network Segmentation for IoT
|
|-- Emerging Trends
| |-- AI and Machine Learning in Cybersecurity
| | |-- AI-Based Threat Detection
| | |-- Automating Incident Response
| |-- Zero Trust Architecture
| | |-- Principles of Zero Trust
| | |-- Implementing Zero Trust in an Organization
|
|-- Soft Skills
| |-- Communication and Collaboration
| | |-- Reporting Security Incidents
| | |-- Collaboration with Other Departments
| |-- Ethical Hacking
| | |-- Red Teaming and Blue Teaming
| | |-- Bug Bounty Programs

Free CyberSecurity Courses For FREE
Link 1: intercomuniversity.com/free-courses/artificial-intelligence/microsofts-secret-stash-of-100-free-courses-you-need-to-know-about/

Link 2: Hacking manuals: t.me/zerotrusthackers/236