How to search on Google from another region

I Search From helps you simulate using a Google search from another location or device, or search with custom search settings.

The site allows you to build your search query so that Google searches by the territorial data you specify.

You can choose from search region and device type to city.

#lifehack #security

16 Websites to Find Remote International Jobs

1. LinkedIn - linkedin.com
2. Indeed - indeed.com
3. Glassdoor - glassdoor.com
4. FlexJobs - flexjobs.com
5. Remote.co - remote.co
6. Upwork - upwork.com
7. Freelancer - freelancer.com
8. Fiverr - fiverr.com
9. Guru - guru.com
10. Toptal - toptal.com
11. AngelList - angel.co
12. SimplyHired - simplyhired.com
13. Remotive - remotive.com
14. Hired - hired.com
15. CloudPeeps - cloudpeeps.com
16. TaskRabbit - taskrabbit.com

10 awesome frontend development YouTube channels:

1. Traversy Media 🚀
2. The Net Ninja 🥷
3. Dev Ed 🎨
4. Academind 📚
5. Fireship 🔥
6. Codevolution 💻
7. DesignCourse 🎨
8. Florin Pop 🧑‍💻
9. Web Dev Simplified 🌐
10. Kevin Powell 🎥

@Javascript_courses

Social media is increasingly just social videos

🚀 New Telegram Channel Alert!
We've discovered a gem for AI enthusiasts: AI.News.Daily!

Why follow AI.News.Daily?

It's your go-to for:
• Daily AI Breakthroughs: Get updates every day.
• Expert Insights: Learn from the best with easy-to-follow advice.
• Monetization Tips: Discover how to turn AI into profit.

Top picks from the channel:

• Anthropic’s Free AI Courses: Access a treasure trove of free courses and guides tailored for Claude.
• AI Catalogs: Explore the top 10 AI catalogs on GitHub.
• Free Prompt Libraries: Stay creative with the 6 best AI prompt libraries.
• Website Building: Discover top AI solutions for creating websites effortlessly.
• AI Selection Tool: Compare AIs, check prices, and view detailed graphs and leaderboards.

There’s so much more waiting for you!

👉Don’t miss out: @ainews_daily

Artificial Intelligence, Game Theory and Mechanism Design in Politics
Tshilidzi Marwala, 2023

🔟 Data Science Project Ideas for Freshers

Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.

Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.

Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.

Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.

Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.

Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).

Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.

Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.

Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.

A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.

Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.

Free datasets to build the projects
👇👇
https://t.iss.one/datasciencefun/1126

ENJOY LEARNING 👍👍

Confused about which field to dive into—Front-End Development (FE), Back-End Development (BE), Machine Learning (ML), or Blockchain?

Here's a concise breakdown of each, designed to clarify your options:

### Front-End Development (FE)
Key Skills:
- HTML/CSS: Fundamental for creating the structure and style of web pages.
- JavaScript: Essential for adding interactivity and functionality to websites.
- Frameworks/Libraries: React, Angular, or Vue.js for efficient and scalable front-end development.
- Responsive Design: Ensuring websites look good on all devices.
- Version Control: Git for managing code changes and collaboration.

Career Prospects:
- Web Developer
- UI/UX Designer
- Front-End Engineer

### Back-End Development (BE)
Key Skills:
- Programming Languages: Python, Java, Ruby, Node.js, or PHP for server-side logic.
- Databases: SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data management.
- APIs: RESTful and GraphQL for communication between front-end and back-end.
- Server Management: Understanding of server, network, and hosting environments.
- Security: Knowledge of authentication, authorization, and data protection.

Career Prospects:
- Back-End Developer
- Full-Stack Developer
- Database Administrator

### Machine Learning (ML)
Key Skills:
- Programming Languages: Python and R are widely used in ML.
- Mathematics: Statistics, linear algebra, and calculus for understanding ML algorithms.
- Libraries/Frameworks: TensorFlow, PyTorch, Scikit-Learn for building ML models.
- Data Handling: Pandas, NumPy for data manipulation and preprocessing.
- Model Evaluation: Techniques for assessing model performance.

Career Prospects:
- Data Scientist
- Machine Learning Engineer
- AI Researcher

### Blockchain
Key Skills:
- Cryptography: Understanding of encryption and security principles.
- Blockchain Platforms: Ethereum, Hyperledger, Binance Smart Chain for building decentralized applications.
- Smart Contracts: Solidity for developing smart contracts.
- Distributed Systems: Knowledge of peer-to-peer networks and consensus algorithms.
- Blockchain Tools: Truffle, Ganache, Metamask for development and testing.

Career Prospects:
- Blockchain Developer
- Smart Contract Developer
- Crypto Analyst

### Decision Criteria
1. Interest: Choose an area you are genuinely interested in.
2. Market Demand: Research the current job market to see which skills are in demand.
3. Career Goals: Consider your long-term career aspirations.
4. Learning Curve: Assess how much time and effort you can dedicate to learning new skills.

Each field offers unique opportunities and challenges, so weigh your options carefully based on your personal preferences and career objectives.

Here are some telegram channels to help you build your career 👇

Web Development
https://t.iss.one/webdevcoursefree

Jobs & Internships
https://t.iss.one/getjobss

Blockchain
https://t.iss.one/Bitcoin_Crypto_Web

Machine Learning
https://t.iss.one/datasciencefun

Artificial Intelligence
https://t.iss.one/machinelearning_deeplearning

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

YouTube is FREE.
ChatGPT is FREE.
Podcasts are FREE.
Google Gemini is FREE.
Udemy is FREE

The internet is free education.

Start learning, building, and earning.

SOME USEFUL  WEBSITES  ONLINE EDUCATIONAL SUPPORT

www.khanacademy.org
www.academicearths.org
www.coursera.com
www.edx.org
www.open2study.com
www.academicjournals.org
codeacademy.org
youtube.com/education


BOOK SITES
www.bookboon.com
https://ebookee.org
https://sharebookfree.com
https://m.freebooks.com
www.obooko.com
www.manybooks.net
www.epubbud.com
www.bookyards.com
www.getfreeebooks.com
https://freecomputerbooks.com
www.essays.se
www.sparknotes.com
www.pink.monkey.com



ANSWERS TO QUESTIONS
www.ehow.com
www.whatis.com
www.howstuffwork.com
www.webopedia.com
www.plagtracker.com
www.answers.com


SEARCH SITES
■ About.com (www.about.com)
■ AllTheWeb (www.alltheweb.com)
■ AltaVista (www.altavista.com)
■ Ask Jeeves! (www.askjeeves.com)
■ Excite (www.excite.com)
■ HotBot (www.hotbot.com)
■ LookSmart (www.looksmart.com)
■ Lycos (www.lycos.com)
■ Open Directory (www.dmoz.org)
■ Google (www.google.com)
■ Mamma (www.mamma.com)
■ Webcrawler (www.webcrawler.com)
■ Aol (www.aol.com)
■ Dogpile (www.dogpile.com)
■ 10pht (www.10pht.com)


SEARCHING FOR PEOPLE
■ AnyWho (www.anywho.com)
■ InfoSpace (www.infospace.com)
■ Switchboard (www.switchboard.com)
■ WhitePages.com (www.whitepages.com)
■ WhoWhere (www.whowhere.lycos.com)


SEARCHING FOR THE LATEST NEWS
■ ABC News (www.abcnews.com)
■ CBS News (www.cbsnews.com)
■ CNN (www.cnn.com)
■ Fox News (www.foxnews.com)
■ MSNBC (www.msnbc.com)
■ New York Times (www.nytimes.com)
■ USA Today (www.usatoday.com)


SEARCHING FOR SPORTS HEADLINES AND SCORES
■ CBS SportsLine (www.sportsline.com)
■ CNN/Sports Illustrated (sportsillustrated.cnn.com)
■ ESPN.com (espn.go.com)
■ FOXSports (foxsports.lycos.com)
■ NBC Sports (www.nbcsports.com)
■ The Sporting News (www.sportingnews.com)


SEARCHING FOR MEDICAL INFORMATION
■ healthAtoZ.com (www.healthatoz.com)
■ kidsDoctor (www.kidsdoctor.com)
■ MedExplorer (www.medexplorer.com)
■ MedicineNet (www.medicinenet.com)
■ National Library of Medicine
(www.nlm.nih.gov)
■ Planet Wellness (www.planetwellness.com)
■ WebMD Health (my.webmd.com)

𝗔𝗣𝗜 𝗧𝗲𝗿𝗺𝗶𝗻𝗼𝗹𝗼𝗴𝘆 𝗛𝗮𝗻𝗱𝗯𝗼𝗼𝗸 😻

12 Fundamental Math Theories Needed to Understand AI

1. Curse of Dimensionality
This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data.
2. Law of Large Numbers
A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods.
3. Central Limit Theorem
This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning.
4. Bayes’ Theorem
A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI.
5. Overfitting and Underfitting
Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance.
6. Gradient Descent
This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models.
7. Information Theory
Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency.
8. Markov Decision Processes (MDP)
MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents.
9. Game Theory
Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments.
10. Statistical Learning Theory
This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions.
11. Hebbian Theory
This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory.
12. Convolution (Kernel)
Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.

Programming is no longer about how well you google search.

Programming is now about how well you can write prompts for an AI system to generate code for you, and you validate it.