๐๐ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐
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๐ Enroll today & start your AI journey!
AI is the future now & highly in demand
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๐ Enroll today & start your AI journey!
โค4
TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
โค1
One day or Day one. You decide.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
Data Science edition.
๐ข๐ป๐ฒ ๐๐ฎ๐ : I will learn SQL.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Download mySQL Workbench.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will build my projects for my portfolio.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Look on Kaggle for a dataset to work on.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will master statistics.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Start the free Khan Academy Statistics and Probability course.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will learn to tell stories with data.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Install Tableau Public and create my first chart.
๐ข๐ป๐ฒ ๐๐ฎ๐: I will become a Data Scientist.
๐๐ฎ๐ ๐ข๐ป๐ฒ: Update my resume and apply to some Data Science job postings.
โค1
AI tools for job hunt.pdf
2.4 MB
AI Tools
15 built in hooks react js.pdf
454.5 KB
React Js โก๏ธ 15 Built in hooksโ
๏ธ
โค5
Breaking into Data Analytics doesnโt need to be complicated.
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Jumping into advanced tools like Hadoop or Spark before mastering the basics.
๐ซ Focusing only on tools, not on business problem-solving.
๐ซ Collecting certificates instead of solving real problems.
๐ซ Thinking you need to know everything from SQL to machine learning right away.
Instead:
โ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
โ Learn how to clean, explore, and interpret data to solve business questions.
โ Understand core concepts like KPIs, dashboards, and business metrics.
โ Pick real datasets and analyze them with clear goals and insights.
โ Build a portfolio that shows you can translate data into decisions.
React โค๏ธ for more
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Jumping into advanced tools like Hadoop or Spark before mastering the basics.
๐ซ Focusing only on tools, not on business problem-solving.
๐ซ Collecting certificates instead of solving real problems.
๐ซ Thinking you need to know everything from SQL to machine learning right away.
Instead:
โ Start with Excel, SQL, and one visualization tool (like Power BI or Tableau).
โ Learn how to clean, explore, and interpret data to solve business questions.
โ Understand core concepts like KPIs, dashboards, and business metrics.
โ Pick real datasets and analyze them with clear goals and insights.
โ Build a portfolio that shows you can translate data into decisions.
React โค๏ธ for more
โค1
๐ Become an Agentic AI Builder โ Free 12โWeek Certification by Ready Tensor
Ready Tensorโs Agentic AI Developer Certification is a free, project first 12โweek program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building โ each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
๐ Apply now: https://www.readytensor.ai/agentic-ai-cert/
Ready Tensorโs Agentic AI Developer Certification is a free, project first 12โweek program designed to help you build and deploy real-world agentic AI systems. You'll complete three portfolio-ready projects using tools like LangChain, LangGraph, and vector databases, while deploying production-ready agents with FastAPI or Streamlit.
The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building โ each project is reviewed against rigorous standards.
You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.
๐ Apply now: https://www.readytensor.ai/agentic-ai-cert/
โค1
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐๐ฅ๐๐ ๐๐ฒ๐บ๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ๐ฐ๐น๐ฎ๐๐ ๐ถ๐ป ๐๐๐ฑ๐ฒ๐ฟ๐ฎ๐ฏ๐ฎ๐ฑ/๐ฃ๐๐ป๐ฒ ๐
Looking to become a Data Analyst? Itโs one of the most in-demand roles in tech โ and the best part? No coding required!
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Hurry Up ๐โโ๏ธ! Limited seats are available.
Looking to become a Data Analyst? Itโs one of the most in-demand roles in tech โ and the best part? No coding required!
๐ฅ Learn Data Analytics with Real-time Projects ,Hands-on Tools
โจ Highlights:
โ 100% Placement Support
โ 500+ Hiring Partners
โ Weekly Hiring Drives
๐ฅ๐ฒ๐ด๐ถ๐๐๐ฒ๐ฟ ๐ก๐ผ๐:- ๐
๐น Hyderabad :- https://pdlink.in/4kFhjn3
๐น Pune:- https://pdlink.in/45p4GrC
Hurry Up ๐โโ๏ธ! Limited seats are available.
โค1
Want to become a Data Scientist?
Hereโs a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING ๐๐
#datascience
Hereโs a quick roadmap with essential concepts:
1. Mathematics & Statistics
Linear Algebra: Matrix operations, eigenvalues, eigenvectors, and decomposition, which are crucial for machine learning.
Probability & Statistics: Hypothesis testing, probability distributions, Bayesian inference, confidence intervals, and statistical significance.
Calculus: Derivatives, integrals, and gradients, especially partial derivatives, which are essential for understanding model optimization.
2. Programming
Python or R: Choose a primary programming language for data science.
Python: Libraries like NumPy, Pandas for data manipulation, and Scikit-Learn for machine learning.
R: Especially popular in academia and finance, with libraries like dplyr and ggplot2 for data manipulation and visualization.
SQL: Master querying and database management, essential for accessing, joining, and filtering large datasets.
3. Data Wrangling & Preprocessing
Data Cleaning: Handle missing values, outliers, duplicates, and data formatting.
Feature Engineering: Create meaningful features, handle categorical variables, and apply transformations (scaling, encoding, etc.).
Exploratory Data Analysis (EDA): Visualize data distributions, correlations, and trends to generate hypotheses and insights.
4. Data Visualization
Python Libraries: Use Matplotlib, Seaborn, and Plotly to visualize data.
Tableau or Power BI: Learn interactive visualization tools for building dashboards.
Storytelling: Develop skills to interpret and present data in a meaningful way to stakeholders.
5. Machine Learning
Supervised Learning: Understand algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Support Vector Machines (SVM).
Unsupervised Learning: Study clustering (K-means, DBSCAN) and dimensionality reduction (PCA, t-SNE).
Evaluation Metrics: Understand accuracy, precision, recall, F1-score for classification and RMSE, MAE for regression.
6. Advanced Machine Learning & Deep Learning
Neural Networks: Understand the basics of neural networks and backpropagation.
Deep Learning: Get familiar with Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data.
Transfer Learning: Apply pre-trained models for specific use cases.
Frameworks: Use TensorFlow Keras for building deep learning models.
7. Natural Language Processing (NLP)
Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal.
NLP Techniques: Understand bag-of-words, TF-IDF, and word embeddings (Word2Vec, GloVe).
NLP Models: Work with recurrent neural networks (RNNs), transformers (BERT, GPT) for text classification, sentiment analysis, and translation.
8. Big Data Tools (Optional)
Distributed Data Processing: Learn Hadoop and Spark for handling large datasets. Use Google BigQuery for big data storage and processing.
9. Data Science Workflows & Pipelines (Optional)
ETL & Data Pipelines: Extract, Transform, and Load data using tools like Apache Airflow for automation. Set up reproducible workflows for data transformation, modeling, and monitoring.
Model Deployment: Deploy models in production using Flask, FastAPI, or cloud services (AWS SageMaker, Google AI Platform).
10. Model Validation & Tuning
Cross-Validation: Techniques like K-fold cross-validation to avoid overfitting.
Hyperparameter Tuning: Use Grid Search, Random Search, and Bayesian Optimization to optimize model performance.
Bias-Variance Trade-off: Understand how to balance bias and variance in models for better generalization.
11. Time Series Analysis
Statistical Models: ARIMA, SARIMA, and Holt-Winters for time-series forecasting.
Time Series: Handle seasonality, trends, and lags. Use LSTMs or Prophet for more advanced time-series forecasting.
12. Experimentation & A/B Testing
Experiment Design: Learn how to set up and analyze controlled experiments.
A/B Testing: Statistical techniques for comparing groups & measuring the impact of changes.
ENJOY LEARNING ๐๐
#datascience
โค2
๐๐ญ๐๐ซ๐ญ ๐๐จ๐ฎ๐ซ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ญ๐ข๐๐ฌ ๐๐จ๐ฎ๐ซ๐ง๐๐ฒ โ ๐๐๐% ๐
๐ซ๐๐ & ๐๐๐ ๐ข๐ง๐ง๐๐ซ-๐
๐ซ๐ข๐๐ง๐๐ฅ๐ฒ๐
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
Want to dive into data analytics but donโt know where to start?๐งโ๐ปโจ๏ธ
These free Microsoft learning paths take you from analytics basics to creating dashboards, AI insights with Copilot, and end-to-end analytics with Microsoft Fabric.๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/47oQD6f
No prior experience needed โ just curiosityโ ๏ธ
โค1
Top 10 CSS Interview Questions
1. What is CSS and what are its key features?
CSS (Cascading Style Sheets) is a stylesheet language used to describe the presentation of a document written in HTML or XML. Its key features include controlling layout, styling text, setting colors, spacing, and more, allowing for a separation of content and design for better maintainability and flexibility.
2. Explain the difference between inline, internal, and external CSS.
- Inline CSS is applied directly within an HTML element using the
- Internal CSS is defined within a
- External CSS is linked to an HTML document via the
3. What is the CSS box model and what are its components?
The CSS box model describes the rectangular boxes generated for elements in the document tree and consists of four components:
- Content: The actual content of the element.
- Padding: The space between the content and the border.
- Border: The edge surrounding the padding.
- Margin: The space outside the border that separates the element from others.
4. How do you center a block element horizontally using CSS?
To center a block element horizontally, you can use the
5. What are CSS selectors and what are the different types?
CSS selectors are patterns used to select elements to apply styles. The different types include:
- Universal selector (
- Element selector (
- Class selector (
- ID selector (
- Attribute selector (
- Pseudo-class selector (
- Pseudo-element selector (
6. Explain the difference between
-
-
-
-
7. What is Flexbox and how is it used in CSS?
Flexbox (Flexible Box Layout) is a layout model that allows for more efficient arrangement of elements within a container. It is used to align and distribute space among items in a container, even when their size is unknown or dynamic. Flexbox is enabled by setting
8. How do you create a responsive design in CSS?
Responsive design can be achieved using media queries, flexible grid layouts, and relative units like percentages,
9. What are CSS preprocessors and name a few popular ones.
CSS preprocessors extend CSS with variables, nested rules, and functions, making it more powerful and easier to maintain. Popular CSS preprocessors include:
- Sass (Syntactically Awesome Style Sheets)
- LESS (Leaner Style Sheets)
- Stylus
10. How do you implement CSS animations?
CSS animations are implemented using the
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
1. What is CSS and what are its key features?
CSS (Cascading Style Sheets) is a stylesheet language used to describe the presentation of a document written in HTML or XML. Its key features include controlling layout, styling text, setting colors, spacing, and more, allowing for a separation of content and design for better maintainability and flexibility.
2. Explain the difference between inline, internal, and external CSS.
- Inline CSS is applied directly within an HTML element using the
style
attribute.- Internal CSS is defined within a
<style>
tag inside the <head>
section of an HTML document.- External CSS is linked to an HTML document via the
<link>
tag and is written in a separate .css
file.3. What is the CSS box model and what are its components?
The CSS box model describes the rectangular boxes generated for elements in the document tree and consists of four components:
- Content: The actual content of the element.
- Padding: The space between the content and the border.
- Border: The edge surrounding the padding.
- Margin: The space outside the border that separates the element from others.
4. How do you center a block element horizontally using CSS?
To center a block element horizontally, you can use the
margin: auto;
property. For example:.center {
width: 50%;
margin: auto;
}
5. What are CSS selectors and what are the different types?
CSS selectors are patterns used to select elements to apply styles. The different types include:
- Universal selector (
*
)- Element selector (
element
)- Class selector (
.class
)- ID selector (
#id
)- Attribute selector (
[attribute]
)- Pseudo-class selector (
:pseudo-class
)- Pseudo-element selector (
::pseudo-element
)6. Explain the difference between
absolute
, relative
, fixed
, and sticky
positioning in CSS.-
relative
: The element is positioned relative to its normal position.-
absolute
: The element is positioned relative to its nearest positioned ancestor or the initial containing block if none exists.-
fixed
: The element is positioned relative to the viewport and does not move when the page is scrolled.-
sticky
: The element is treated as relative until a given offset position is met in the viewport, then it behaves as fixed.7. What is Flexbox and how is it used in CSS?
Flexbox (Flexible Box Layout) is a layout model that allows for more efficient arrangement of elements within a container. It is used to align and distribute space among items in a container, even when their size is unknown or dynamic. Flexbox is enabled by setting
display: flex;
on a container element.8. How do you create a responsive design in CSS?
Responsive design can be achieved using media queries, flexible grid layouts, and relative units like percentages,
em
, and rem
. Media queries adjust styles based on the viewport's width, height, and other characteristics. For example:@media (max-width: 600px) {
.container {
width: 100%;
}
}
9. What are CSS preprocessors and name a few popular ones.
CSS preprocessors extend CSS with variables, nested rules, and functions, making it more powerful and easier to maintain. Popular CSS preprocessors include:
- Sass (Syntactically Awesome Style Sheets)
- LESS (Leaner Style Sheets)
- Stylus
10. How do you implement CSS animations?
CSS animations are implemented using the
@keyframes
rule to define the animation and the animation
property to apply it to an element. For example:@keyframes example {
from {background-color: red;}
to {background-color: yellow;}
}
.element {
animation: example 5s infinite;
}
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING ๐๐
โค1
๐ ๐ง๐ผ๐ฝ ๐ฑ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ | ๐๐ป๐ฟ๐ผ๐น๐น ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ ๐
๐ Upgrade your career with in-demand tech skills & FREE certifications!
1๏ธโฃ AI & ML โ https://pdlink.in/3U3eZuq
2๏ธโฃ Data Analytics โ https://pdlink.in/4lp7hXQ
3๏ธโฃ Cloud Computing โ https://pdlink.in/3GtNJlO
4๏ธโฃ Cyber Security โ https://pdlink.in/4nHBuTh
5๏ธโฃ More Courses โ https://pdlink.in/3ImMFAB
๐ 100% FREE | Certificates Provided | Learn Anytime, Anywhere
๐ Upgrade your career with in-demand tech skills & FREE certifications!
1๏ธโฃ AI & ML โ https://pdlink.in/3U3eZuq
2๏ธโฃ Data Analytics โ https://pdlink.in/4lp7hXQ
3๏ธโฃ Cloud Computing โ https://pdlink.in/3GtNJlO
4๏ธโฃ Cyber Security โ https://pdlink.in/4nHBuTh
5๏ธโฃ More Courses โ https://pdlink.in/3ImMFAB
๐ 100% FREE | Certificates Provided | Learn Anytime, Anywhere
โค2