𝟳 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗖𝗼𝘀𝘁, 𝗡𝗼 𝗖𝗮𝘁𝗰𝗵!)😍

Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌

From Python and machine learning to hands-on projects and challenges🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4dAuymr

Enjoy Learning ✅️

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.

𝗕𝗿𝗲𝗮𝗸 𝗜𝗻𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗥𝗘𝗘 𝗠𝗜𝗧 𝗖𝗼𝘂𝗿𝘀𝗲😍

If you’re serious about AI, you can’t skip Deep Learning—and this FREE course from MIT is one of the best ways to start👨‍💻📌

Offered by MIT’s top researchers and engineers, this online course is open to everyone, no matter where you live or work🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3H6cggR

Why wait to get started when you can learn from MIT for free?✅️

𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟱 😍

Data Analytics :- https://pdlink.in/3Fq7E4p

Data Science :- https://pdlink.in/4iSWjaP

SQL :- https://pdlink.in/3EyjUPt

Python :- https://pdlink.in/4c7hGDL

Web Dev :- https://bit.ly/4ffFnJZ

AI :- https://pdlink.in/4d0SrTG

Enroll For FREE & Get Certified 🎓

𝟰 𝗙𝗿𝗲𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗗𝗮𝗶𝗹𝘆 (𝗡𝗼 𝗦𝗶𝗴𝗻𝘂𝗽 𝗡𝗲𝗲𝗱𝗲𝗱!)😍

🚀 Want to Sharpen Your Data Analytics Skills for FREE?💫

If you’re learning data analytics and want to build real skills, theory alone won’t cut it. You need hands-on practice—and the best part? You can do it daily, for free!🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/44WK6ie

Enjoy Learning ✅️

5 GitHub Repo to Master Python

1. The Algorithms: https://github.com/TheAlgorithms/Python
2. Vinta: https://github.com/vinta/awesome-python
3. Avinash Kranjan: https://tinyurl.com/Amazing-Python-Scripts
4. Geek Computers: https://github.com/geekcomputers/Python
5. Practical Tutorials: https://tinyurl.com/project-based-learningg

Don’t forget to react ❤️ if you’d like to see more content like this!

Thank you all for joining! ❤️🙏

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Google :- https://pdlink.in/3H2YJX7

Microsoft :- https://pdlink.in/4iq8QlM

Infosys :- https://pdlink.in/4jsHZXf

IBM :- https://pdlink.in/3QyJyqk

Cisco :- https://pdlink.in/4fYr1xO

Enroll For FREE & Get Certified 🎓

If you're serious about getting into Data Science with Python, follow this 5-step roadmap.

Each phase builds on the previous one, so don’t rush.

Take your time, build projects, and keep moving forward.

Step 1: Python Fundamentals
Before anything else, get your hands dirty with core Python.
This is the language that powers everything else.

✅ What to learn:
type(), int(), float(), str(), list(), dict()
if, elif, else, for, while, range()
def, return, function arguments
List comprehensions: [x for x in list if condition]
– Mini Checkpoint:
Build a mini console-based data calculator (inputs, basic operations, conditionals, loops).

Step 2: Data Cleaning with Pandas
Pandas is the tool you'll use to clean, reshape, and explore data in real-world scenarios.

✅ What to learn:
Cleaning: df.dropna(), df.fillna(), df.replace(), df.drop_duplicates()
Merging & reshaping: pd.merge(), df.pivot(), df.melt()
Grouping & aggregation: df.groupby(), df.agg()
– Mini Checkpoint:
Build a data cleaning noscript for a messy CSV file. Add comments to explain every step.

Step 3: Data Visualization with Matplotlib
Nobody wants raw tables.
Learn to tell stories through charts.

✅ What to learn:
Basic charts: plt.plot(), plt.scatter()
Advanced plots: plt.hist(), plt.kde(), plt.boxplot()
Subplots & customizations: plt.subplots(), fig.add_subplot(), plt.noscript(), plt.legend(), plt.xlabel()
– Mini Checkpoint:
Create a dashboard-style notebook visualizing a dataset, include at least 4 types of plots.

Step 4: Exploratory Data Analysis (EDA)
This is where your analytical skills kick in.
You’ll draw insights, detect trends, and prepare for modeling.

✅ What to learn:
Denoscriptive stats: df.mean(), df.median(), df.mode(), df.std(), df.var(), df.min(), df.max(), df.quantile()
Correlation analysis: df.corr(), plt.imshow(), scipy.stats.pearsonr()
— Mini Checkpoint:
Write an EDA report (Markdown or PDF) based on your findings from a public dataset.

Step 5: Intro to Machine Learning with Scikit-Learn
Now that your data skills are sharp, it's time to model and predict.

✅ What to learn:
Training & evaluation: train_test_split(), .fit(), .predict(), cross_val_score()
Regression: LinearRegression(), mean_squared_error(), r2_score()
Classification: LogisticRegression(), accuracy_score(), confusion_matrix()
Clustering: KMeans(), silhouette_score()

– Final Checkpoint:

Build your first ML project end-to-end
✅ Load data
✅ Clean it
✅ Visualize it
✅ Run EDA
✅ Train & test a model
✅ Share the project with visuals and explanations on GitHub

Don’t just complete tutorialsm create things.

Explain your work.
Build your GitHub.
Write a blog.

That’s how you go from “learning” to “landing a job

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍

𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

🚀 Learn In-Demand Tech Skills for Free — Certified by Microsoft!

These free Microsoft-certified online courses are perfect for beginners, students, and professionals looking to upskill

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3Hio2Vg

Enroll For FREE & Get Certified🎓️