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
โค3
Forwarded from Generative AI
๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฒ ๐๐ป-๐๐ฒ๐บ๐ฎ๐ป๐ฑ ๐ฆ๐ธ๐ถ๐น๐น๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐!๐
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!๐จโ๐ป
No need for expensive coursesโstart learning for FREE today!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Ddxd7P
Donโt miss this opportunityโstart learning today and take your skills to the next level!โ ๏ธ
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!๐จโ๐ป
No need for expensive coursesโstart learning for FREE today!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3Ddxd7P
Donโt miss this opportunityโstart learning today and take your skills to the next level!โ ๏ธ
โค1
๐ง๐ต๐ฒ ๐๐ฒ๐๐ ๐๐ฟ๐ฒ๐ฒ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ต๐ฒ๐ฎ๐ ๐ฆ๐ต๐ฒ๐ฒ๐ ๐ผ๐ป ๐๐ถ๐๐๐๐ฏ ๐๐๐ฒ๐ฟ๐ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ ๐ฆ๐ต๐ผ๐๐น๐ฑ ๐๐ผ๐ผ๐ธ๐บ๐ฎ๐ฟ๐ธ๐
๐ง Master Data Science Faster with This Free GitHub Cheat Sheet๐
Whether youโre starting your data science journey or preparing for job interviews, having the right revision tool can make all the difference๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4klQmF3
Must-have resource for students and professionalsโ ๏ธ
๐ง Master Data Science Faster with This Free GitHub Cheat Sheet๐
Whether youโre starting your data science journey or preparing for job interviews, having the right revision tool can make all the difference๐ฏ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4klQmF3
Must-have resource for students and professionalsโ ๏ธ
๐๐ฆ๐ฉ๐จ๐ซ๐ญ๐ข๐ง๐ ๐๐๐๐๐ฌ๐ฌ๐๐ซ๐ฒ ๐๐ข๐๐ซ๐๐ซ๐ข๐๐ฌ:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
๐๐จ๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ญ๐๐ฌ๐๐ญ:
df = pd.read_csv('your_dataset.csv')
๐๐ง๐ข๐ญ๐ข๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐ฌ๐ฉ๐๐๐ญ๐ข๐จ๐ง:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐๐๐ฅ๐ฎ๐๐ฌ:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
๐๐จ๐๐๐ข๐ง๐ ๐ญ๐ก๐ ๐๐๐ญ๐๐ฌ๐๐ญ:
df = pd.read_csv('your_dataset.csv')
๐๐ง๐ข๐ญ๐ข๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐ฌ๐ฉ๐๐๐ญ๐ข๐จ๐ง:
1- View the first few rows:
df.head()
2- Summary of the dataset:
df.info()
3- Statistical summary:
df.describe()
๐๐๐ง๐๐ฅ๐ข๐ง๐ ๐๐ข๐ฌ๐ฌ๐ข๐ง๐ ๐๐๐ฅ๐ฎ๐๐ฌ:
1- Identify missing values:
df.isnull().sum()
2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()
๐๐๐ญ๐ ๐๐ข๐ฌ๐ฎ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง:
1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()
2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()
3- Pair plots:
sns.pairplot(df)
plt.show()
4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()
๐๐๐ญ๐๐ ๐จ๐ซ๐ข๐๐๐ฅ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ข๐ฌ:
Count plots for categorical features:
plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
โค1
Forwarded from Artificial Intelligence
๐ฑ ๐ ๐๐๐-๐๐ผ๐น๐น๐ผ๐ ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐๐ฝ๐ถ๐ฟ๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐๐ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
Want to Become a Data Scientist in 2025? Start Here!๐ฏ
If youโre serious about becoming a Data Scientist in 2025, the learning doesnโt have to be expensive โ or boring!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring prosโ ๏ธ
Want to Become a Data Scientist in 2025? Start Here!๐ฏ
If youโre serious about becoming a Data Scientist in 2025, the learning doesnโt have to be expensive โ or boring!๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring prosโ ๏ธ
๐จโ๐ป ๐ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ค๐ข๐ฅ๐ฅ๐ฌ ๐๐ฏ๐๐ซ๐ฒ ๐๐๐ญ๐ ๐๐ง๐๐ฅ๐ฒ๐ฌ๐ญ ๐๐๐๐๐ฌ ๐ข๐ง ๐๐ง ๐๐ซ๐ ๐๐ง๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐
๐ธ๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ & ๐๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).
๐ธ๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.
๐ธ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ข๐ง๐ ๐๐จ๐๐๐ฅ๐ฌ
Itโs important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your modelโs performance.
๐ธ๐ ๐๐ฆ๐ข๐ฅ๐ข๐๐ซ๐ข๐ญ๐ฒ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.
๐ธ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ข๐ง๐ ๐๐จ๐๐๐ฅ๐ฌ
Once youโve built a model, itโs important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.
๐ ๐๐ซ๐จ ๐๐ข๐ฉ: Keep practicing by working on real projects or using online platforms to improve these skills!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
๐ธ๐๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ & ๐๐ง๐ฌ๐ฎ๐ฉ๐๐ซ๐ฏ๐ข๐ฌ๐๐ ๐๐๐๐ซ๐ง๐ข๐ง๐
You need to understand two main types of machine learning: supervised learning (used for predicting outcomes, like whether a customer will buy a product) and unsupervised learning (used to find patterns, like grouping customers based on buying behavior).
๐ธ๐ ๐๐๐ญ๐ฎ๐ซ๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ๐ข๐ง๐
This is about turning raw data into useful information for your model. Knowing how to clean data, fill missing values, and create new features will improve the model's performance.
๐ธ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ข๐ง๐ ๐๐จ๐๐๐ฅ๐ฌ
Itโs important to know how to check if a model is working well. Use simple measures like accuracy (how often the model is right), precision, and recall to assess your modelโs performance.
๐ธ๐ ๐๐ฆ๐ข๐ฅ๐ข๐๐ซ๐ข๐ญ๐ฒ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ฌ
Get to know basic machine learning algorithms like Decision Trees, Random Forests, and K-Nearest Neighbors (KNN). These are often used for solving real-world problems and can help you choose the best approach.
๐ธ๐๐๐ฉ๐ฅ๐จ๐ฒ๐ข๐ง๐ ๐๐จ๐๐๐ฅ๐ฌ
Once youโve built a model, itโs important to know how to use it in the real world. Learn how to deploy models so they can be used by others in your organization and continue to make decisions automatically.
๐ ๐๐ซ๐จ ๐๐ข๐ฉ: Keep practicing by working on real projects or using online platforms to improve these skills!
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
โค3
Forwarded from Artificial Intelligence
๐ ๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ ๐ณ๐ฟ๐ผ๐บ ๐๐ฎ๐ฟ๐๐ฎ๐ฟ๐ฑ, ๐ฆ๐๐ฎ๐ป๐ณ๐ผ๐ฟ๐ฑ, ๐ ๐๐ง & ๐๐ผ๐ผ๐ด๐น๐ฒ๐
Why pay thousands when you can access world-class Computer Science courses for free? ๐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills๐จโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchersโ ๏ธ
Why pay thousands when you can access world-class Computer Science courses for free? ๐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills๐จโ๐๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchersโ ๏ธ
โค2
Coding isn't easy!
Itโs the art of turning ideas into functional, impactful software that shapes the world around us.
To truly excel in coding, focus on these key areas:
0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.
1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.
2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.
3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.
4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation scripts.
5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.
6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.
7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.
8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.
9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.
๐ก Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING ๐๐
Itโs the art of turning ideas into functional, impactful software that shapes the world around us.
To truly excel in coding, focus on these key areas:
0. Understanding the Basics: Learn the syntax, variables, loops, and conditionals in your chosen programming language. These are the building blocks of coding.
1. Mastering Data Structures and Algorithms: These are the backbone of efficient, scalable, and optimized code.
2. Learning Debugging Techniques: Understand how to identify and fix errors in your code using tools and logical thinking.
3. Writing Clean Code: Follow best practices like commenting, indentation, and naming conventions to make your code readable and maintainable.
4. Building Real-World Projects: Hands-on experience is essential. Apply what you learn by building applications, games, or automation scripts.
5. Collaborating with Git: Master version control to work effectively in teams and manage your codebase.
6. Exploring Frameworks and Libraries: Learn to use tools that simplify coding and add functionality to your projects.
7. Understanding Problem-Solving: Focus on logical thinking and breaking down problems into smaller, manageable parts.
8. Adapting to New Technologies: Stay curious and keep learning new languages, paradigms, and tools as they emerge.
9. Practicing Consistently: Coding is a skill that improves with regular practice and perseverance.
๐ก Embrace the process, learn from your mistakes, and keep pushing your limits to grow as a developer.
Best Programming Resources: https://topmate.io/coding/886839
ENJOY LEARNING ๐๐
โค3
Forwarded from Artificial Intelligence
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ฐ๐ ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ ๐ผ๐ป ๐ฌ๐ผ๐๐ง๐๐ฏ๐ฒ โ ๐๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐ฃ๐น๐ฎ๐๐น๐ถ๐๐ ๐๐๐ถ๐ฑ๐ฒ๐
๐ฅ YouTube is the ultimate free classroomโand this is your Data Analytics syllabus in one post!๐จโ๐ป
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-readyโจ๏ธ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jzVggc
Enjoy Learning โ ๏ธ
๐ฅ YouTube is the ultimate free classroomโand this is your Data Analytics syllabus in one post!๐จโ๐ป
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-readyโจ๏ธ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jzVggc
Enjoy Learning โ ๏ธ
๐ฅ Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1๏ธโฃ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2๏ธโฃ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3๏ธโฃ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
โญ๏ธ 41.4k stars on Github
๐ https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1๏ธโฃ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2๏ธโฃ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3๏ธโฃ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
โญ๏ธ 41.4k stars on Github
๐ https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
โค3
Forwarded from Artificial Intelligence
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These 3 Free SQL resources will help you go from beginner to job-readyโwithout spending a single rupee! ๐โจ
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Looking to master SQL for Data Analytics or prep for your dream tech job? ๐ผ
These 3 Free SQL resources will help you go from beginner to job-readyโwithout spending a single rupee! ๐โจ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3TcvfsA
๐ฅ Start learning today and build the skills top companies want!โ ๏ธ
Creative ways to craft your data analytics portfolio
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Free Data sets for Data Analytics Projects: https://t.iss.one/DataPortfolio
1. Storytelling with Data Projects: Craft narratives around real-world scenarios, demonstrating your ability to extract insights from data. Use visuals, such as charts and graphs, to make your analysis more engaging.
2. Interactive Dashboards: Build interactive dashboards using tools like Tableau or Power BI. Showcase your skills in creating user-friendly interfaces that allow for dynamic exploration of data.
3. Predictive Modeling Showcase: Develop projects that involve predictive modeling, such as machine learning algorithms. Highlight your ability to make data-driven predictions and explain the implications of your findings.
4. Data Visualization Blog: Start a blog to share your insights and showcase your projects. Explain your analysis process, display visualizations, and discuss the impact of your findings. This demonstrates your ability to communicate complex ideas.
5. Open Source Contributions: Contribute to data-related open-source projects on platforms like GitHub. This not only adds to your portfolio but also demonstrates collaboration skills and engagement with the broader data science community.
6. Kaggle Competitions: Participate in Kaggle competitions and document your approach and results. Employ a variety of algorithms and techniques to solve different types of problems, showcasing your versatility.
7. Industry-specific Analyses: Tailor projects to specific industries of interest. For example, analyze trends in healthcare, finance, or marketing. This demonstrates your understanding of domain-specific challenges and your ability to provide actionable insights.
8. Portfolio Website: Create a professional portfolio website to showcase your projects. Include project descriptions, methodologies, visualizations, and the impact of your analyses. Make it easy for potential employers to navigate and understand your work.
9. Skill Diversification: Showcase a range of skills by incorporating data cleaning, feature engineering, and other pre-processing steps into your projects. Highlighting a holistic approach to data analysis enhances your portfolio.
10. Continuous Learning Projects: Demonstrate your commitment to ongoing learning by including projects that showcase new tools, techniques, or methodologies you've recently acquired. This shows adaptability and a proactive attitude toward staying current in the field.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค1
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
๐ญ๐ฌ๐ฌ% ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
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10 New & Trending AI Concepts You Should Know in 2025
โ Retrieval-Augmented Generation (RAG) โ Combines search with generative AI for smarter answers
โ Multi-Modal Models โ AI that understands text, image, audio, and video (like GPT-4V, Gemini)
โ Agents & AutoGPT โ AI that can plan, execute, and make decisions with minimal input
โ Synthetic Data Generation โ Creating fake yet realistic data to train AI models
โ Federated Learning โ Train models without moving your data (privacy-first AI)
โ Prompt Engineering โ Crafting prompts to get the best out of LLMs
โ Fine-Tuning & LoRA โ Customize big models for specific tasks with minimal resources
โ AI Safety & Alignment โ Making sure AI systems behave ethically and predictably
โ TinyML โ Running ML models on edge devices with very low power (IoT focus)
โ Open-Source LLMs โ Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
โ Retrieval-Augmented Generation (RAG) โ Combines search with generative AI for smarter answers
โ Multi-Modal Models โ AI that understands text, image, audio, and video (like GPT-4V, Gemini)
โ Agents & AutoGPT โ AI that can plan, execute, and make decisions with minimal input
โ Synthetic Data Generation โ Creating fake yet realistic data to train AI models
โ Federated Learning โ Train models without moving your data (privacy-first AI)
โ Prompt Engineering โ Crafting prompts to get the best out of LLMs
โ Fine-Tuning & LoRA โ Customize big models for specific tasks with minimal resources
โ AI Safety & Alignment โ Making sure AI systems behave ethically and predictably
โ TinyML โ Running ML models on edge devices with very low power (IoT focus)
โ Open-Source LLMs โ Rise of models like Mistral, LLaMA, Mixtral challenging closed-source giants
Free AI Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
ENJOY LEARNING ๐๐
โค2
Forwarded from Artificial Intelligence
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ ๐๐ง ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐ฌ๐ผ๐ ๐๐ฎ๐ป ๐ง๐ฎ๐ธ๐ฒ ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
๐No MIT Admission? No Problem โ Learn from MIT for Free!๐ฅ
MIT is known for world-class educationโbut you donโt need to walk its halls to access its knowledge๐๐
๐๐ข๐ง๐ค๐:-
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These courses offer industry-relevant skills & completion certificates at no costโ ๏ธ
๐No MIT Admission? No Problem โ Learn from MIT for Free!๐ฅ
MIT is known for world-class educationโbut you donโt need to walk its halls to access its knowledge๐๐
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These courses offer industry-relevant skills & completion certificates at no costโ ๏ธ
โค2๐ฅ1
Want To become a Backend Developer?
Hereโs a roadmap with essential concepts:
1. Programming Languages
JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.
2. Version Control
Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.
3. Databases
Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.
4. APIs & Web Services
REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.
5. Server & Application Frameworks
Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.
6. Authentication & Authorization
JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.
7. Caching
Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.
8. Message Queues & Event-Driven Architecture
Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.
9. Microservices & Distributed Systems
Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).
10. Testing & Debugging
Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.
11. Containerization & Orchestration
Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.
12. CI/CD (Continuous Integration & Continuous Deployment)
CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.
13. Cloud Platforms
AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.
14. Logging & Monitoring
Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.
15. Security
Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.
16. Scalability & Optimization
Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.
ENJOY LEARNING ๐๐
#backend
Hereโs a roadmap with essential concepts:
1. Programming Languages
JavaScript (Node.js), Python, Java, Ruby, Go, or PHP: Pick one language and get comfortable with syntax & basics.
2. Version Control
Git: Learn version control basics, commit changes, branching, and collaboration on GitHub/GitLab.
3. Databases
Relational Databases: Master SQL basics with databases like MySQL or PostgreSQL. Learn how to design schemas, write efficient queries, and perform joins.
NoSQL Databases: Understand when to use NoSQL (MongoDB, Cassandra) vs. SQL. Learn data modeling for NoSQL.
4. APIs & Web Services
REST APIs: Learn how to create, test, and document RESTful services using tools like Postman.
GraphQL: Gain an understanding of querying and mutation, and when GraphQL may be preferred over REST.
gRPC: Explore gRPC for high-performance communication between services if your stack supports it.
5. Server & Application Frameworks
Frameworks: Master backend frameworks in your chosen language (e.g., Express for Node.js, Django for Python, Spring Boot for Java).
Routing & Middleware: Learn how to structure routes, manage requests, and use middleware.
6. Authentication & Authorization
JWT: Learn how to manage user sessions and secure APIs using JSON Web Tokens.
OAuth2: Understand OAuth2 for third-party authentication (e.g., Google, Facebook).
Session Management: Learn to implement secure session handling and token expiration.
7. Caching
Redis or Memcached: Learn caching to optimize performance, improve response times, and reduce load on databases.
Browser Caching: Set up HTTP caching headers for browser caching of static resources.
8. Message Queues & Event-Driven Architecture
Message Brokers: Learn message queues like RabbitMQ, Kafka, or AWS SQS for handling asynchronous processes.
Pub/Sub Pattern: Understand publish/subscribe patterns for decoupling services.
9. Microservices & Distributed Systems
Microservices Design: Understand service decomposition, inter-service communication, and Bounded Contexts.
Distributed Systems: Learn fundamentals like the CAP Theorem, data consistency models, and resiliency patterns (Circuit Breaker, Bulkheads).
10. Testing & Debugging
Unit Testing: Master unit testing for individual functions.
Integration Testing: Test interactions between different parts of the system.
End-to-End (E2E) Testing: Simulate real user scenarios to verify application behavior.
Debugging: Use logs, debuggers, and tracing to locate and fix issues.
11. Containerization & Orchestration
Docker: Learn how to containerize applications for easy deployment and scaling.
Kubernetes: Understand basics of container orchestration, scaling, and management.
12. CI/CD (Continuous Integration & Continuous Deployment)
CI/CD Tools: Familiarize yourself with tools like Jenkins, GitHub Actions, or GitLab CI/CD.
Automated Testing & Deployment: Automate tests, builds, and deployments for rapid development cycles.
13. Cloud Platforms
AWS, Azure, or Google Cloud: Learn basic cloud services such as EC2 (compute), S3 (storage), and RDS (databases).
Serverless Functions: Explore serverless options like AWS Lambda for on-demand compute resources.
14. Logging & Monitoring
Centralized Logging: Use tools like ELK Stack (Elasticsearch, Logstash, Kibana) for aggregating and analyzing logs.
Monitoring & Alerting: Implement real-time monitoring with Prometheus, Grafana, or CloudWatch.
15. Security
Data Encryption: Encrypt data at rest and in transit using SSL/TLS and other encryption standards.
Secure Coding: Protect against common vulnerabilities (SQL injection, XSS, CSRF).
Zero Trust Architecture: Learn to design systems with the principle of least privilege and regular authentication.
16. Scalability & Optimization
Load Balancing: Distribute traffic evenly across servers.
Database Optimization: Learn indexing, sharding, and partitioning.
Horizontal vs. Vertical Scaling: Know when to scale by adding resources to existing servers or by adding more servers.
ENJOY LEARNING ๐๐
#backend
โค1
Forwarded from Python Projects & Resources
๐ฑ ๐๐ฅ๐๐ ๐ ๐๐ง ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ง๐ฒ๐ฐ๐ต, ๐๐ & ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ๐
Dreaming of an MIT education without the tuition fees? ๐ฏ
These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data scienceโall from the comfort of your home! ๐โจ
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Your gateway to a smarter careerโ ๏ธ
Dreaming of an MIT education without the tuition fees? ๐ฏ
These 5 FREE courses from MIT will help you master the fundamentals of programming, AI, machine learning, and data scienceโall from the comfort of your home! ๐โจ
๐๐ข๐ง๐ค๐:-
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Your gateway to a smarter careerโ ๏ธ
Evolution of Programming Languages๐ฅ๏ธ
๐ฐProgramming Languages๐ฐ
1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.
2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesnโt load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javascript brought about a revolution in webapps.
3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.
4. C:
Its a low level language too thatโs why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.
5. C++ :
Its C with more features and those features make it more complex.
6. Perl:
A language which was developed to create small scripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.
7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.
8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.
9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.
10. PHP:
It is used to process things in a website. It is server-sided language as it doesnโt get executed in user browser, but on the server. It can be used to generate dynamic webpage content.
11. SQL:
This is not exactly a programming language. It is used to interact with databases.
โก๏ธ This list could be long because there are too many programming language but I introduced you to the popular ones.
โWhich Language Should Be Your First Programming Language?
โ Suggestions..
1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.
2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).
3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesnโt start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you donโt need to compile the script to run it, just write one and run it.
Join for more: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
๐ฐProgramming Languages๐ฐ
1. JAVA:
More than 85% android apps are created using JAVA. It is also used in big (big means big) websites. It is a portable programming language which makes it easy to use on multi platforms.
2. Java Script:
Its a browser/client side language. It makes the webpage more interactive. Like for example when you enter a comment on Facebook then the whole page doesnโt load., just that comment is added. This kind of functionalities are added into webpages with JavaScript. Javascript brought about a revolution in webapps.
3. Assembly Language:
The most low level programming language because its nothing more than machine code written in human readable form. Its hard to write and you need to have deep understanding of computers to use this because you are really talking with it. Its very fast in terms of execution.
4. C:
Its a low level language too thatโs why its fast. It is used to program operating system, computer games and software which need to be fast. It is hard to write but gives you more control of your computer.
5. C++ :
Its C with more features and those features make it more complex.
6. Perl:
A language which was developed to create small scripts easily . Programming in Perl is easy and efficient but the programs are comparatively slower.
7. Python:
Perl was made better and named Python. Its easy, efficient and flexible. You can automate things with python in a go.
8. Ruby:
Its similar to Python but it became popular when they created a web application development framework named Rails which lets developers to write their web application conveniently.
9. HTML and CSS:
HTML and CSS are languages not programming languages because they are just used display things on a website. They do not do any actual processing. HTML is used to create the basic structure of the website and then CSS is used to make it look good.
10. PHP:
It is used to process things in a website. It is server-sided language as it doesnโt get executed in user browser, but on the server. It can be used to generate dynamic webpage content.
11. SQL:
This is not exactly a programming language. It is used to interact with databases.
โก๏ธ This list could be long because there are too many programming language but I introduced you to the popular ones.
โWhich Language Should Be Your First Programming Language?
โ Suggestions..
1. Getting Started
Learn HTML & CSS. They are easy and will give you a basic idea of how programming works. You will be able to create your own webpages. After HTML you can go with PHP and SQL, so will have a good grasp over web designing and then you can go with python, C or Java. I assure you that PHP, HTML and SQL will be definitely useful in your hacking journey.
2. Understanding Computer And Programming Better
C..The classic C! C is one of the most foundational languages. If you learn C, you will have a deep knowledge of Computers and you will have a greater understanding of programming too, that will make you a better programmer. You will spend most of your time compiling though (just trying to crack a joke).
3. Too Eager To Create Programs?
Python! Python is very easy to learn and you can create a program which does something instead of programming calculators. Well Python doesnโt start you from the basics but with if you know python, you will be able to understand other languages better. One benefit of python is that you donโt need to compile the script to run it, just write one and run it.
Join for more: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
โค2
Forwarded from Python Projects & Resources
๐ฑ ๐ฃ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐๐ถ๐๐๐๐ฏ ๐ฅ๐ฒ๐ฝ๐ผ๐๐ถ๐๐ผ๐ฟ๐ถ๐ฒ๐ ๐๐ผ ๐ ๐ฎ๐๐๐ฒ๐ฟ ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒ๐
Looking to Master Python for Free?โจ๏ธ
These 5 GitHub repositories are all you need to level up โ from beginner to advanced! ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FG7DcW
๐ Save this post & share it with a Python learner!
Looking to Master Python for Free?โจ๏ธ
These 5 GitHub repositories are all you need to level up โ from beginner to advanced! ๐ป
๐๐ข๐ง๐ค๐:-
https://pdlink.in/3FG7DcW
๐ Save this post & share it with a Python learner!