๐๐๐๐๐๐ ๐
๐๐๐ ๐๐ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ฎ๐ซ๐ฌ๐๐ฌ ๐
Transform your skills with these cutting-edge courses by NVIDIA.
Check out the following NVIDIA FREE AI Certification Courses
๐๐ข๐ง๐ค๐:-
https://bit.ly/3YXv0nY
Enroll For FREE & Get Certified ๐
Transform your skills with these cutting-edge courses by NVIDIA.
Check out the following NVIDIA FREE AI Certification Courses
๐๐ข๐ง๐ค๐:-
https://bit.ly/3YXv0nY
Enroll For FREE & Get Certified ๐
โค3๐2
Creating a data science portfolio is a great way to showcase your skills and experience to potential employers. Here are some steps to help you create a strong data science portfolio:
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
1. Choose relevant projects: Select a few data science projects that demonstrate your skills and interests. These projects can be from your previous work experience, personal projects, or online competitions.
2. Clean and organize your code: Make sure your code is well-documented, organized, and easy to understand. Use comments to explain your thought process and the steps you took in your analysis.
3. Include a variety of projects: Try to include a mix of projects that showcase different aspects of data science, such as data cleaning, exploratory data analysis, machine learning, and data visualization.
4. Create visualizations: Data visualizations can help make your portfolio more engaging and easier to understand. Use tools like Matplotlib, Seaborn, or Tableau to create visually appealing charts and graphs.
5. Write project summaries: For each project, provide a brief summary of the problem you were trying to solve, the dataset you used, the methods you applied, and the results you obtained. Include any insights or recommendations that came out of your analysis.
6. Showcase your technical skills: Highlight the programming languages, libraries, and tools you used in each project. Mention any specific techniques or algorithms you implemented.
7. Link to your code and data: Provide links to your code repositories (e.g., GitHub) and any datasets you used in your projects. This allows potential employers to review your work in more detail.
8. Keep it updated: Regularly update your portfolio with new projects and skills as you gain more experience in data science. This will show that you are actively engaged in the field and continuously improving your skills.
By following these steps, you can create a comprehensive and visually appealing data science portfolio that will impress potential employers and help you stand out in the competitive job market.
๐2
To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
Hi guys,
Join our main channel for all data science resources and free courses ๐๐
https://t.iss.one/datasciencefun
Join our main channel for all data science resources and free courses ๐๐
https://t.iss.one/datasciencefun
Telegram
Data Science & Machine Learning
Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data
For collaborations: @love_data
โค2๐1
๐ Free useful resources to learn Machine Learning
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐ Google
https://developers.google.com/machine-learning/crash-course
๐ Leetcode
https://leetcode.com/explore/featured/card/machine-learning-101
๐ Hackerrank
https://www.hackerrank.com/domains/ai/machine-learning
๐ Hands-on Machine Learning
https://t.iss.one/datasciencefun/424
๐ FreeCodeCamp
https://www.freecodecamp.org/learn/machine-learning-with-python/
๐ Machine learning projects
https://t.iss.one/datasciencefun/392
๐ Kaggle
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning
๐ Geeksforgeeks
https://www.geeksforgeeks.org/machine-learning/
๐ Create ML Models
https://docs.microsoft.com/en-us/learn/paths/create-machine-learn-models/
๐ Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
Join @free4unow_backup for more free resources
ENJOY LEARNING ๐๐
๐5โค2
For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content
ENJOY LEARNING ๐๐
๐4โค1
To learn data structures and algorithms in Python, you can follow these steps:
1. Start with the basics: Learn about the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs. Learn how to implement them in Python and understand their time and space complexities.
2. Study algorithms: Study the most common algorithms for searching, sorting, and traversing data structures. Understand their time and space complexities and the trade-offs between different algorithms.
3. Practice, practice, practice: The more you practice implementing data structures and algorithms, the better you will get at it. You can start by solving problems on websites like LeetCode and HackerRank, or by working on small projects of your own.
4. Read and learn from others: Read articles and blogs written by experts in the field, and learn from their experiences and insights. Follow the work of other Python developers on Github, and see how they use data structures and algorithms in their projects.
1. Start with the basics: Learn about the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs. Learn how to implement them in Python and understand their time and space complexities.
2. Study algorithms: Study the most common algorithms for searching, sorting, and traversing data structures. Understand their time and space complexities and the trade-offs between different algorithms.
3. Practice, practice, practice: The more you practice implementing data structures and algorithms, the better you will get at it. You can start by solving problems on websites like LeetCode and HackerRank, or by working on small projects of your own.
4. Read and learn from others: Read articles and blogs written by experts in the field, and learn from their experiences and insights. Follow the work of other Python developers on Github, and see how they use data structures and algorithms in their projects.
โค1
An important collection of the 15 best machine learning cheat sheets.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
ENJOY LEARNING ๐๐
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
ENJOY LEARNING ๐๐
๐ฅ4๐2
The Untold Truth About Junior Data Analyst Interviews (From Someone Whoโs Seen It All)
Guys, letโs cut through the noise. Most companies arenโt testing how many fancy tools you knowโtheyโre testing how you think! Hereโs what you really need to focus on:
SQL Interview Round
WHAT YOU THINK THEY WANT:
โWrite the most complex SQL queries!โ
WHAT THEY ACTUALLY TEST:
Can you clean messy data?
Do you handle NULL values logically?
How do you deal with duplicates?
Can you explain what you did, step-by-step?
Do you verify your results?
REALISTIC QUESTIONS YOUโLL FACE:
1๏ธโฃ Find duplicate orders in a sales table.
2๏ธโฃ Calculate monthly revenue for the past year.
3๏ธโฃ Identify the top 10 customers by revenue.
Excel Interview Round
WHAT YOU THINK THEY WANT:
โShow off crazy Excel skills with macros and VBA.โ
WHAT THEY REALLY WANT TO SEE:
Your ability to use VLOOKUP/XLOOKUP.
Comfort with Pivot Tables for summarization.
Your knack for creating basic formulas for data cleaning.
A logical approach to tackling Excel problems.
REALISTIC TASKS:
โ Merge two datasets using VLOOKUP.
โ Summarize sales trends in a Pivot Table.
โ Clean up inconsistent text fields (hello, TRIM function).
Business Case Analysis
WHAT YOU THINK THEY WANT:
โBuild a mind-blowing dashboard or deliver complex models.โ
WHAT THEY ACTUALLY EVALUATE:
Can you break down the problem into manageable parts?
Do you ask smart, relevant questions?
Is your analysis focused on business outcomes?
How clearly can you present your findings?
What You'll Definitely Face
1. The โData Messโ Scenario
Theyโll hand you a messy dataset with:
Missing data, duplicates, and weird formats.
No clear instructions.
They watch:
๐ How you approach the problem.
๐ If you spot inconsistencies.
๐ The steps you take to clean and structure data.
2. The โExplain Your Analysisโ Challenge
Theyโll say:
โWalk us through what you did and why.โ
Theyโre looking for:
Clarity in communication.
Your thought process.
The connection between your work and the business context.
How to Stand Out in Interviews
1. Nail the Basics
SQL: Focus on joins, filtering, grouping, and aggregating.
Excel: Get comfortable with lookups, pivots, and cleaning techniques.
Data Cleaning: Practice handling real-world messy datasets.
2. Understand the Business
Research their industry and common metrics (e.g., sales, churn rate).
Know basic KPIs they might ask about.
Prepare thoughtful, strategic questions.
3. Practice Real Scenarios
๐น Analyze trends: Monthly revenue, churn analysis.
๐น Segment customers: Who are your top spenders?
๐น Evaluate campaigns: Which marketing effort drove the best ROI?
Reality Check: What Really Matters
๐ How you think through a problem.
๐ How you communicate your insights.
๐ How you connect your work to business goals.
๐ซ What doesnโt matter?
Writing overly complex SQL.
Knowing every Excel formula.
Advanced machine learning knowledge (for most junior roles).
Pro Tip: Stay calm, ask questions, and show youโre eager to solve problems. Your mindset is just as important as your technical skills!
Like this post if you want me to post more useful content โค๏ธ
Hope it helps :)
Guys, letโs cut through the noise. Most companies arenโt testing how many fancy tools you knowโtheyโre testing how you think! Hereโs what you really need to focus on:
SQL Interview Round
WHAT YOU THINK THEY WANT:
โWrite the most complex SQL queries!โ
WHAT THEY ACTUALLY TEST:
Can you clean messy data?
Do you handle NULL values logically?
How do you deal with duplicates?
Can you explain what you did, step-by-step?
Do you verify your results?
REALISTIC QUESTIONS YOUโLL FACE:
1๏ธโฃ Find duplicate orders in a sales table.
2๏ธโฃ Calculate monthly revenue for the past year.
3๏ธโฃ Identify the top 10 customers by revenue.
Excel Interview Round
WHAT YOU THINK THEY WANT:
โShow off crazy Excel skills with macros and VBA.โ
WHAT THEY REALLY WANT TO SEE:
Your ability to use VLOOKUP/XLOOKUP.
Comfort with Pivot Tables for summarization.
Your knack for creating basic formulas for data cleaning.
A logical approach to tackling Excel problems.
REALISTIC TASKS:
โ Merge two datasets using VLOOKUP.
โ Summarize sales trends in a Pivot Table.
โ Clean up inconsistent text fields (hello, TRIM function).
Business Case Analysis
WHAT YOU THINK THEY WANT:
โBuild a mind-blowing dashboard or deliver complex models.โ
WHAT THEY ACTUALLY EVALUATE:
Can you break down the problem into manageable parts?
Do you ask smart, relevant questions?
Is your analysis focused on business outcomes?
How clearly can you present your findings?
What You'll Definitely Face
1. The โData Messโ Scenario
Theyโll hand you a messy dataset with:
Missing data, duplicates, and weird formats.
No clear instructions.
They watch:
๐ How you approach the problem.
๐ If you spot inconsistencies.
๐ The steps you take to clean and structure data.
2. The โExplain Your Analysisโ Challenge
Theyโll say:
โWalk us through what you did and why.โ
Theyโre looking for:
Clarity in communication.
Your thought process.
The connection between your work and the business context.
How to Stand Out in Interviews
1. Nail the Basics
SQL: Focus on joins, filtering, grouping, and aggregating.
Excel: Get comfortable with lookups, pivots, and cleaning techniques.
Data Cleaning: Practice handling real-world messy datasets.
2. Understand the Business
Research their industry and common metrics (e.g., sales, churn rate).
Know basic KPIs they might ask about.
Prepare thoughtful, strategic questions.
3. Practice Real Scenarios
๐น Analyze trends: Monthly revenue, churn analysis.
๐น Segment customers: Who are your top spenders?
๐น Evaluate campaigns: Which marketing effort drove the best ROI?
Reality Check: What Really Matters
๐ How you think through a problem.
๐ How you communicate your insights.
๐ How you connect your work to business goals.
๐ซ What doesnโt matter?
Writing overly complex SQL.
Knowing every Excel formula.
Advanced machine learning knowledge (for most junior roles).
Pro Tip: Stay calm, ask questions, and show youโre eager to solve problems. Your mindset is just as important as your technical skills!
Like this post if you want me to post more useful content โค๏ธ
Hope it helps :)
๐ฅ4๐3
In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
Here are some scenarios where using multiple scalers can be helpful in a data science project:
1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.
2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.
3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.
4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.
5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.
When using multiple scalers in a data science project, it's important to evaluate the impact of scaling on the model performance through cross-validation or other evaluation methods. Try experimenting with different scaling techniques to you find the optimal approach for your specific dataset and machine learning model.
5 Algorithms you must know as a data scientist ๐ฉโ๐ป ๐งโ๐ป
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/free4unow_backup
Like if you need similar content ๐๐
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
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Some useful PYTHON libraries for data science
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.
NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms, advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++
SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.
Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook โpylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.
Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Pythonโs usage in data scientist community.
Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.
Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.
Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.
Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.
Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.
Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.
SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.
Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.
Additional libraries, you might need:
os for Operating system and file operations
networkx and igraph for graph based data manipulations
regular expressions for finding patterns in text data
BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.