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I just signed up for Hack the Future: A Gen AI Sprint Powered by Dataโa nationwide hackathon where you'll tackle real-world challenges using Data and AI. Itโs a golden opportunity to work with industry experts, participate in hands-on workshops, and win exciting prizes.
Highly recommended for working professionals looking to upskill or transition into the AI/Data space.
If you're looking to level up your skills, network with like-minded folks, and boost your career, don't miss out!
Register now: https://gfgcdn.com/tu/UO5/
๐1
๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐ผ๐๐ฒ๐ฟ ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ & ๐๐น๐ฒ๐๐ฎ๐๐ฒ ๐ฌ๐ผ๐๐ฟ ๐๐ฎ๐๐ต๐ฏ๐ผ๐ฎ๐ฟ๐ฑ ๐๐ฎ๐บ๐ฒ!๐
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
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Enjoy Learning โ ๏ธ
Want to turn raw data into stunning visual stories?๐
Here are 6 FREE Power BI courses thatโll take you from beginner to proโwithout spending a single rupee๐ฐ
๐๐ข๐ง๐ค๐:-
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Enjoy Learning โ ๏ธ
๐1
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ข๐ป ๐๐ฒ๐๐ผ๐ฝ๐๐
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Interview questions asked by top product-based companies.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
๐จ๐พโ๐ป Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
โถ๏ธ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
๐ Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If youโre stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
A friend of mine recently shared their interview journey, and I'd like to pass on what I learned about the data structures and algorithms (DSA) rounds.
๐จ๐พโ๐ป Data Structures: He encountered questions on topics like arrays, strings, matrices, stacks, queues, and different types of linked lists (singly, doubly, and circular).
โถ๏ธ Algorithms: He was also interviewed on a wide array of algorithms like linear search, binary search, and sorting algorithms (bubble, quick, merge).
And faced questions on more challenging subjects like Greedy algorithms, Dynamic programming, and Graph algorithms.
๐ Specifics: The devil lies in the details! His interview also delved into advanced topics such as Advanced Data Structures, Pattern Searching, Recursion, Backtracking, and Divide and Conquer strategies.
However, your ability to apply these concepts to real-world situations will undoubtedly set you apart from others.
On top, If youโre stuck at any of the above questions and need the right guidance in cracking top product-based company interviews,
As a community of tech enthusiasts, let's share our own interview experiences in the comments below. Together, we can learn from each other's experiences.
๐3
๐๐ฐ๐ฐ๐ฒ๐ป๐๐๐ฟ๐ฒ ๐๐ฒ๐ป๐๐ ๐๐ฎ๐ฐ๐ธ๐ฎ๐๐ต๐ผ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ ๐
Hack the Future: Join the Data and AI Revolution
In collaboration with Accenture and with GeeksforGeeks as the Community Partner, this event offers a unique opportunity to collaborate, learn, and innovate.
Whether you're an AI engineer, business analyst, or someone passionate about building a career in Data and AI,
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With exciting cash prizes and networking opportunities, it's the perfect platform to join the Data and AI revolution.
Donโt miss outโbe part of shaping the future!
Hack the Future: Join the Data and AI Revolution
In collaboration with Accenture and with GeeksforGeeks as the Community Partner, this event offers a unique opportunity to collaborate, learn, and innovate.
Whether you're an AI engineer, business analyst, or someone passionate about building a career in Data and AI,
๐๐ข๐ง๐ค ๐:-
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With exciting cash prizes and networking opportunities, it's the perfect platform to join the Data and AI revolution.
Donโt miss outโbe part of shaping the future!
Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
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๐
๐๐๐ ๐๐๐ฌ๐ญ๐๐ซ๐๐ฅ๐๐ฌ๐ฌ ๐๐ง ๐๐๐ญ๐๐ฌ๐ญ ๐๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ข๐๐ฌ๐
- AI/ML
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- Business Analytics
- Data Science
- Fullstack
- UI/UX
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- Fullstack
- UI/UX
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๐ฉโ๐ซ๐งโ๐ซ PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME.
โ๏ธ[ Web Developer]
โ๏ธ[ Game Developer]
โ๏ธ[ Data Analysis]
โ๏ธ[ Desktop Developer]
โ๏ธ[ Embedded System Program]
โ๏ธ[Mobile Apps Development]
โ๏ธ[ Web Developer]
PHP, C#, JS, JAVA, Python, Rubyโ๏ธ[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#โ๏ธ[ Data Analysis]
R, Matlab, Java, Pythonโ๏ธ[ Desktop Developer]
Java, C#, C++, Pythonโ๏ธ[ Embedded System Program]
C, Python, C++ โ๏ธ[Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#โค1๐1
๐๐ป๐ณ๐ผ๐๐๐ ๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resumeโat no cost.
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Complete Data Science Roadmap
๐๐
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Like if you need similar content ๐๐
๐๐
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Like if you need similar content ๐๐
๐4
๐ฑ ๐๐ฅ๐๐ ๐ง๐ฒ๐ฐ๐ต ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ฟ๐ผ๐บ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐, ๐๐ช๐ฆ, ๐๐๐ , ๐๐ถ๐๐ฐ๐ผ, ๐ฎ๐ป๐ฑ ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ. ๐
- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning
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- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning
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Enroll For FREE & Get Certified ๐
5 Handy Tips to Master Data Science โฌ๏ธ
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
1๏ธโฃ Begin with introductory projects that cover the fundamental concepts of data science, such as data exploration, cleaning, and visualization. These projects will help you get familiar with common data science tools and libraries like Python (Pandas, NumPy, Matplotlib), R, SQL, and Excel
2๏ธโฃ Look for publicly available datasets from sources like Kaggle, UCI Machine Learning Repository. Working with real-world data will expose you to the challenges of messy, incomplete, and heterogeneous data, which is common in practical scenarios.
3๏ธโฃ Explore various data science techniques like regression, classification, clustering, and time series analysis. Apply these techniques to different datasets and domains to gain a broader understanding of their strengths, weaknesses, and appropriate use cases.
4๏ธโฃ Work on projects that involve the entire data science lifecycle, from data collection and cleaning to model building, evaluation, and deployment. This will help you understand how different components of the data science process fit together.
5๏ธโฃ Consistent practice is key to mastering any skill. Set aside dedicated time to work on data science projects, and gradually increase the complexity and scope of your projects as you gain more experience.
๐2โค1
๐๐ฅ๐๐ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ข๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ( ๐๐๐๐ถ๐ป๐ฒ๐๐ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐)๐
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๐1
๐ Web development project ideas for beginners
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
ENJOY LEARNING ๐๐
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
Free Resources to learn web development https://t.iss.one/free4unow_backup/554
ENJOY LEARNING ๐๐
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Learn skills in Data Science & AI designed to enable your career success
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Learn skills in Data Science & AI designed to enable your career success
- Artificial Intelligence
- Machine Learning
- Data Analytics
- SQL
- Data Science
- Generative AI
๐๐ข๐ง๐ค ๐:-
https://pdlink.in/41VIuSA
Enroll Now & Get a course completion certificate๐
Want to use ChatGPT at lightning speed?
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
You must tap in to ChatGPT's short cuts.
1. Go to ChatGPT
2. Bottom right '?' mark
3. Access keyboard shortcuts
Keyboard Shortcuts:
1. Show shortcuts: Ctrl + /
2. Focus chat input: Shift + Esc
3. Toggle sidebar: Ctrl + Shift + S
4. Open new chat: Ctrl + Shift + O
5. Copy last response: Ctrl + Shift + C
For example:
"Write a paper from ChatGPT's output."
1. Copy output: Ctrl + Shift + C
2. Open new chat: Ctrl + Shift + O
3. Ask it to write a paper on the info.
4. Ctrl V to paste in new information.
5. Press enter. Then paper completed.
(without ever touching your mouse)
Now THIS is ChatGPT mastery.
Move fast. Save time.
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๐ฐ Cmd command lines pdf ๐
React โค๏ธ for more
React โค๏ธ for more
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Want to learn AI from the best without spending a rupee?
These 5 FREE courses from Harvard and Stanford will help you understand Artificial Intelligence, Deep Learning, NLP, and moreโstraight from the experts๐
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Want to learn AI from the best without spending a rupee?
These 5 FREE courses from Harvard and Stanford will help you understand Artificial Intelligence, Deep Learning, NLP, and moreโstraight from the experts๐
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Top 10 programming languages & frameworks for beginner web developers:
1. HTML/CSS โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
7. Node.js โ JavaScript backend runtime
8. React โ Popular frontend library
9. Angular โ Framework for building dynamic UIs
10. Bootstrap โ Simplifies responsive design
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
1. HTML/CSS โ Basics of web structure & styling
2. JavaScript โ Adds interactivity
3. Python โ Backend & versatility
4. PHP โ Server-side scripting
5. SQL โ Database management
6. Ruby on Rails โ Easy backend framework
7. Node.js โ JavaScript backend runtime
8. React โ Popular frontend library
9. Angular โ Framework for building dynamic UIs
10. Bootstrap โ Simplifies responsive design
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
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