𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀: 𝟱 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆😍
Want to break into Data Science but don’t know where to begin?👨💻📌
You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SU5FJ0
No prior experience needed!✅️
Want to break into Data Science but don’t know where to begin?👨💻📌
You’re not alone. Data Science is one of the most in-demand fields today, but with so many courses online, it can feel overwhelming.💫📲
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SU5FJ0
No prior experience needed!✅️
For those of you who are new to Neural Networks, let me try to give you a brief overview.
Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:
1. Structure: Neural networks have three main types of layers:
- Input layer: Receives the initial data.
- Hidden layers: Intermediate layers that process the input data through weighted connections.
- Output layer: Produces the final output or prediction.
2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer.
3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves:
- Forward pass: Calculating the output based on current weights.
- Loss calculation: Comparing the output to the actual result using a loss function.
- Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent.
4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns.
5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others.
Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data.
30 Days of Data Science: https://news.1rj.ru/str/datasciencefun/1704
Like if you want me to continue data science series 😄❤️
ENJOY LEARNING 👍👍
Neural networks are computational models inspired by the human brain's structure and function. They consist of interconnected layers of nodes (or neurons) that process data and learn patterns. Here's a brief overview:
1. Structure: Neural networks have three main types of layers:
- Input layer: Receives the initial data.
- Hidden layers: Intermediate layers that process the input data through weighted connections.
- Output layer: Produces the final output or prediction.
2. Neurons and Connections: Each neuron receives input from several other neurons, processes this input through a weighted sum, and applies an activation function to determine the output. This output is then passed to the neurons in the next layer.
3. Training: Neural networks learn by adjusting the weights of the connections between neurons using a process called backpropagation, which involves:
- Forward pass: Calculating the output based on current weights.
- Loss calculation: Comparing the output to the actual result using a loss function.
- Backward pass: Adjusting the weights to minimize the loss using optimization algorithms like gradient descent.
4. Activation Functions: Functions like ReLU, Sigmoid, or Tanh are used to introduce non-linearity into the network, enabling it to learn complex patterns.
5. Applications: Neural networks are used in various fields, including image and speech recognition, natural language processing, and game playing, among others.
Overall, neural networks are powerful tools for modeling and solving complex problems by learning from data.
30 Days of Data Science: https://news.1rj.ru/str/datasciencefun/1704
Like if you want me to continue data science series 😄❤️
ENJOY LEARNING 👍👍
❤4😁1
Forwarded from Artificial Intelligence
𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄😍
𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk
𝗝𝗮𝘃𝗮 :- https://pdlink.in/4dWkAMf
𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :- https://pdlink.in/4dFem3o
𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw
Get Your Dream Tech Job In Your Dream Company💫
𝗦𝗤𝗟:- https://pdlink.in/3SMHxaZ
𝗣𝘆𝘁𝗵𝗼𝗻 :- https://pdlink.in/3FJhizk
𝗝𝗮𝘃𝗮 :- https://pdlink.in/4dWkAMf
𝗗𝗦𝗔 :- https://pdlink.in/3FsDA8j
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4jLOJ2a
𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 :- https://pdlink.in/4dFem3o
𝗖𝗼𝗱𝗶𝗻𝗴 :- https://pdlink.in/3F00oMw
Get Your Dream Tech Job In Your Dream Company💫
Cost of living (monthly expenses) for one person by country:
🇨🇭 Switzerland: $3,900
🇳🇴 Norway: $3,200
🇮🇸 Iceland: $3,000
🇯🇵 Japan: $2,800
🇱🇺 Luxembourg: $2,700
🇩🇰 Denmark: $2,650
🇸🇬 Singapore: $2,600
🇮🇪 Ireland: $2,500
🇺🇸 United States: $2,450
🇭🇰 Hong Kong: $2,400
🇫🇮 Finland: $2,350
🇦🇪 UAE: $2,300
🇬🇧 UK: $2,250
🇸🇪 Sweden: $2,200
🇩🇪 Germany: $2,150
🇧🇪 Belgium: $2,100
🇫🇷 France: $2,050
🇳🇱 Netherlands: $2,000
🇨🇦 Canada: $1,950
🇦🇹 Austria: $1,900
🇦🇺 Australia: $1,850
🇳🇿 New Zealand: $1,800
🇨🇭 Switzerland: $3,900
🇳🇴 Norway: $3,200
🇮🇸 Iceland: $3,000
🇯🇵 Japan: $2,800
🇱🇺 Luxembourg: $2,700
🇩🇰 Denmark: $2,650
🇸🇬 Singapore: $2,600
🇮🇪 Ireland: $2,500
🇺🇸 United States: $2,450
🇭🇰 Hong Kong: $2,400
🇫🇮 Finland: $2,350
🇦🇪 UAE: $2,300
🇬🇧 UK: $2,250
🇸🇪 Sweden: $2,200
🇩🇪 Germany: $2,150
🇧🇪 Belgium: $2,100
🇫🇷 France: $2,050
🇳🇱 Netherlands: $2,000
🇨🇦 Canada: $1,950
🇦🇹 Austria: $1,900
🇦🇺 Australia: $1,850
🇳🇿 New Zealand: $1,800
❤3
Let's now understand Data Science Roadmap in detail:
1. Math & Statistics (Foundation Layer)
This is the backbone of data science. Strong intuition here helps with algorithms, ML, and interpreting results.
Key Topics:
Linear Algebra: Vectors, matrices, matrix operations
Calculus: Derivatives, gradients (for optimization)
Probability: Bayes theorem, probability distributions
Statistics: Mean, median, mode, standard deviation, hypothesis testing, confidence intervals
Inferential Statistics: p-values, t-tests, ANOVA
Resources:
Khan Academy (Math & Stats)
"Think Stats" book
YouTube (StatQuest with Josh Starmer)
2. Python or R (Pick One for Analysis)
These are your main tools. Python is more popular in industry; R is strong in academia.
For Python Learn:
Variables, loops, functions, list comprehension
Libraries: NumPy, Pandas, Matplotlib, Seaborn
For R Learn:
Vectors, data frames, ggplot2, dplyr, tidyr
Goal: Be comfortable working with data, writing clean code, and doing basic analysis.
3. Data Wrangling (Data Cleaning & Manipulation)
Real-world data is messy. Cleaning and structuring it is essential.
What to Learn:
Handling missing values
Removing duplicates
String operations
Date and time operations
Merging and joining datasets
Reshaping data (pivot, melt)
Tools:
Python: Pandas
R: dplyr, tidyr
Mini Projects: Clean a messy CSV or scrape and structure web data.
4. Data Visualization (Telling the Story)
This is about showing insights visually for business users or stakeholders.
In Python:
Matplotlib, Seaborn, Plotly
In R:
ggplot2, plotly
Learn To:
Create bar plots, histograms, scatter plots, box plots
Design dashboards (can explore Power BI or Tableau)
Use color and layout to enhance clarity
5. Machine Learning (ML)
Now the real fun begins! Automate predictions and classifications.
Topics:
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM
Unsupervised Learning: Clustering (K-means), PCA
Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC-AUC
Cross-validation, Hyperparameter tuning
Libraries:
scikit-learn, xgboost
Practice On:
Kaggle datasets, Titanic survival, House price prediction
6. Deep Learning & NLP (Advanced Level)
Push your skills to the next level. Essential for AI, image, and text-based tasks.
Deep Learning:
Neural Networks, CNNs, RNNs
Frameworks: TensorFlow, Keras, PyTorch
NLP (Natural Language Processing):
Text preprocessing (tokenization, stemming, lemmatization)
TF-IDF, Word Embeddings
Sentiment Analysis, Topic Modeling
Transformers (BERT, GPT, etc.)
Projects:
Sentiment analysis from Twitter data
Image classifier using CNN
7. Projects (Build Your Portfolio)
Apply everything you've learned to real-world datasets.
Types of Projects:
EDA + ML project on a domain (finance, health, sports)
End-to-end ML pipeline
Deep Learning project (image or text)
Build a dashboard with your insights
Collaborate on GitHub, contribute to open-source
Tips:
Host projects on GitHub
Write about them on Medium, LinkedIn, or personal blog
8. ✅ Apply for Jobs (You're Ready!)
Now, you're prepared to apply with confidence.
Steps:
Prepare your resume tailored for DS roles
Sharpen interview skills (SQL, Python, case studies)
Practice on LeetCode, InterviewBit
Network on LinkedIn, attend meetups
Apply for internships or entry-level DS/DA roles
Keep learning and adapting. Data Science is vast and fast-moving—stay updated via newsletters, GitHub, and communities like Kaggle or Reddit.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content 😄👍
Hope this helps you 😊
1. Math & Statistics (Foundation Layer)
This is the backbone of data science. Strong intuition here helps with algorithms, ML, and interpreting results.
Key Topics:
Linear Algebra: Vectors, matrices, matrix operations
Calculus: Derivatives, gradients (for optimization)
Probability: Bayes theorem, probability distributions
Statistics: Mean, median, mode, standard deviation, hypothesis testing, confidence intervals
Inferential Statistics: p-values, t-tests, ANOVA
Resources:
Khan Academy (Math & Stats)
"Think Stats" book
YouTube (StatQuest with Josh Starmer)
2. Python or R (Pick One for Analysis)
These are your main tools. Python is more popular in industry; R is strong in academia.
For Python Learn:
Variables, loops, functions, list comprehension
Libraries: NumPy, Pandas, Matplotlib, Seaborn
For R Learn:
Vectors, data frames, ggplot2, dplyr, tidyr
Goal: Be comfortable working with data, writing clean code, and doing basic analysis.
3. Data Wrangling (Data Cleaning & Manipulation)
Real-world data is messy. Cleaning and structuring it is essential.
What to Learn:
Handling missing values
Removing duplicates
String operations
Date and time operations
Merging and joining datasets
Reshaping data (pivot, melt)
Tools:
Python: Pandas
R: dplyr, tidyr
Mini Projects: Clean a messy CSV or scrape and structure web data.
4. Data Visualization (Telling the Story)
This is about showing insights visually for business users or stakeholders.
In Python:
Matplotlib, Seaborn, Plotly
In R:
ggplot2, plotly
Learn To:
Create bar plots, histograms, scatter plots, box plots
Design dashboards (can explore Power BI or Tableau)
Use color and layout to enhance clarity
5. Machine Learning (ML)
Now the real fun begins! Automate predictions and classifications.
Topics:
Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM
Unsupervised Learning: Clustering (K-means), PCA
Model Evaluation: Accuracy, Precision, Recall, F1-score, ROC-AUC
Cross-validation, Hyperparameter tuning
Libraries:
scikit-learn, xgboost
Practice On:
Kaggle datasets, Titanic survival, House price prediction
6. Deep Learning & NLP (Advanced Level)
Push your skills to the next level. Essential for AI, image, and text-based tasks.
Deep Learning:
Neural Networks, CNNs, RNNs
Frameworks: TensorFlow, Keras, PyTorch
NLP (Natural Language Processing):
Text preprocessing (tokenization, stemming, lemmatization)
TF-IDF, Word Embeddings
Sentiment Analysis, Topic Modeling
Transformers (BERT, GPT, etc.)
Projects:
Sentiment analysis from Twitter data
Image classifier using CNN
7. Projects (Build Your Portfolio)
Apply everything you've learned to real-world datasets.
Types of Projects:
EDA + ML project on a domain (finance, health, sports)
End-to-end ML pipeline
Deep Learning project (image or text)
Build a dashboard with your insights
Collaborate on GitHub, contribute to open-source
Tips:
Host projects on GitHub
Write about them on Medium, LinkedIn, or personal blog
8. ✅ Apply for Jobs (You're Ready!)
Now, you're prepared to apply with confidence.
Steps:
Prepare your resume tailored for DS roles
Sharpen interview skills (SQL, Python, case studies)
Practice on LeetCode, InterviewBit
Network on LinkedIn, attend meetups
Apply for internships or entry-level DS/DA roles
Keep learning and adapting. Data Science is vast and fast-moving—stay updated via newsletters, GitHub, and communities like Kaggle or Reddit.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like if you need similar content 😄👍
Hope this helps you 😊
❤3
𝟳 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 & 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀😍
💻 You don’t need to spend a rupee to master Python!🐍
Whether you’re an aspiring Data Analyst, Developer, or Tech Enthusiast, these 7 completely free platforms help you go from zero to confident coder👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4l5XXY2
Enjoy Learning ✅️
💻 You don’t need to spend a rupee to master Python!🐍
Whether you’re an aspiring Data Analyst, Developer, or Tech Enthusiast, these 7 completely free platforms help you go from zero to confident coder👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4l5XXY2
Enjoy Learning ✅️
❤1
Lawyers charge for this kind of work. ChatGPT does it for free
Try these 7 prompts:
Try these 7 prompts:
❤3
𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲😍
Dreaming of a career in Data Analytics but don’t know where to begin?
The Career Essentials in Data Analysis program by Microsoft and LinkedIn is a 100% FREE learning path designed to equip you with real-world skills and industry-recognized certification.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kPowBj
Enroll For FREE & Get Certified ✅️
Dreaming of a career in Data Analytics but don’t know where to begin?
The Career Essentials in Data Analysis program by Microsoft and LinkedIn is a 100% FREE learning path designed to equip you with real-world skills and industry-recognized certification.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kPowBj
Enroll For FREE & Get Certified ✅️
🤗 HuggingFace is offering 9 AI courses for FREE!
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
1️⃣ LLM Course:
https://huggingface.co/learn/llm-course/chapter1/1
2️⃣ Agents Course:
https://huggingface.co/learn/agents-course/unit0/introduction
3️⃣ Deep Reinforcement Learning Course:
https://huggingface.co/learn/deep-rl-course/unit0/introduction
4️⃣ Open-Source AI Cookbook:
https://huggingface.co/learn/cookbook/index
5️⃣ Machine Learning for Games Course
https://huggingface.co/learn/ml-games-course/unit0/introduction
6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction
7️⃣ Vision Course:
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
9️⃣ Hugging Face Diffusion Models Course:
https://huggingface.co/learn/diffusion-course/unit0/1
These 9 courses covers LLMs, Agents, Deep RL, Audio and more
1️⃣ LLM Course:
https://huggingface.co/learn/llm-course/chapter1/1
2️⃣ Agents Course:
https://huggingface.co/learn/agents-course/unit0/introduction
3️⃣ Deep Reinforcement Learning Course:
https://huggingface.co/learn/deep-rl-course/unit0/introduction
4️⃣ Open-Source AI Cookbook:
https://huggingface.co/learn/cookbook/index
5️⃣ Machine Learning for Games Course
https://huggingface.co/learn/ml-games-course/unit0/introduction
6️⃣ Hugging Face Audio course:
https://huggingface.co/learn/audio-course/chapter0/introduction
7️⃣ Vision Course:
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
8️⃣ Machine Learning for 3D Course:
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
9️⃣ Hugging Face Diffusion Models Course:
https://huggingface.co/learn/diffusion-course/unit0/1
❤4
Essential Programming Languages to Learn Data Science 👇👇
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).
2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.
3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.
4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.
5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.
6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.
7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.
Free Resources to master data analytics concepts 👇👇
Data Analysis with R
Intro to Data Science
Practical Python Programming
SQL for Data Analysis
Java Essential Concepts
Machine Learning with Python
Data Science Project Ideas
Learning SQL FREE Book
Join @free4unow_backup for more free resources.
ENJOY LEARNING👍👍
❤2
Forwarded from Artificial Intelligence
𝟱 𝗙𝗿𝗲𝗲 𝗚𝗼𝗼𝗴𝗹𝗲 𝗔𝗜 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍
🎓 You don’t need to break the bank to break into AI!🪩
If you’ve been searching for beginner-friendly, certified AI learning—Google Cloud has you covered🤝👨💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SZQRIU
📍All taught by industry-leading instructors✅️
🎓 You don’t need to break the bank to break into AI!🪩
If you’ve been searching for beginner-friendly, certified AI learning—Google Cloud has you covered🤝👨💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3SZQRIU
📍All taught by industry-leading instructors✅️
❤3
Forwarded from Python Projects & Resources
𝗧𝗼𝗽 𝟱 𝗙𝗿𝗲𝗲 𝗞𝗮𝗴𝗴𝗹𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗝𝘂𝗺𝗽𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿😍
Want to break into Data Science but not sure where to start?🚀
These free Kaggle micro-courses are the perfect launchpad — beginner-friendly, self-paced, and yes, they come with certifications!👨🎓🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4l164FN
No subnoscription. No hidden fees. Just pure learning from a trusted platform✅️
Want to break into Data Science but not sure where to start?🚀
These free Kaggle micro-courses are the perfect launchpad — beginner-friendly, self-paced, and yes, they come with certifications!👨🎓🎊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4l164FN
No subnoscription. No hidden fees. Just pure learning from a trusted platform✅️
❤1