𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗠𝘂𝘀𝘁 𝗧𝗮𝗸𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗱 𝗟𝗮𝗻𝗱 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯𝘀!😍

In a world full of competition, your skills will set you apart — not just your degree👨‍🎓📄

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Enjoy Learning ✅️

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Remote Internship at Sony!

Blinkit is hiring Business Analyst 🚀

Experience : 0-2 Years
Location : Bangalore

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HDFC Bank is hiring Data Scientist 🚀

Experience : 2 Years
Location : Mumbai

Apply link : https://www.linkedin.com/jobs/view/4217590214

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍

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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

http://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 👍👍

Check out this job at IBM: https://www.linkedin.com/jobs/view/4202833274

𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘: 𝟰 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗦𝘁𝗮𝗿𝘁 𝗧𝗼𝗱𝗮𝘆!😍

🌟 Want to upgrade your skills without spending a dime? 💻

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All The Best 🎊

Ford hiring Data Scientist

Apply link: https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/job/44845/?utm_medium=jobboard&utm_source=linkedin

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Meesho is hiring Data Scientist 🚀

Experience : 2 Years
Location : Bangalore

Apply link : https://meesho.io/jobs/data-scientist-ii?id=cbc4c285-3c1a-48d9-a57f-7d65f76b9dc4

Flipkart hiring Data Scientist

Apply link: http://www.flipkartcareers.com/#!/job-view/data-scientist-bangalore-karnataka-2024090915375450/?source=linkedin

𝟳 𝗙𝗿𝗲𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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Pocket FM hiring Data Scientist

Apply link: https://www.linkedin.com/jobs/view/4204079017

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Worley are actively hiring for Multiple Data Scientist positions 

Experience : 6 Years to 12 Years

Location - Navi Mumbai

Role & responsibilities


1. Hands-on programming and architecture capabilities in Python.

2. Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and Generative AI related technologies.

3. Experience in implementing and deploying Machine Learning solutions (using various models, such as GPT-4, Lama2, Mistral ai, text embedding ada, Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Topic Modeling, Game Theory etc. )

4. Understanding of Nvidia Enterprise NEMO Suite.

5. Expertise in popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for building, training, and deploying neural network models.

6. Experience in AI solution development with external SaaS products like Azure OCR

7. Experience in the AI/ML components like Azure ML studio, Jupyter Hub, TensorFlow & Sci-Kit Learn

8. Hands-on knowledge of API frameworks.

9. Familiarity with the transformer architecture and its applications in natural language processing (NLP), such as machine translation, text summarization, and question-answering systems.

10. Expertise in designing and implementing CNNs for computer vision tasks, such as image classification, object detection, and semantic segmentation.

11. Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra.

12. Experience with open source software

13. Experience using the cognitive APIs machine learning studios on cloud.

14. Hands-on knowledge of image processing with deep learning ( CNN,RNN,LSTM,GAN)

15. Familiarity with GPU computing and tools like CUDA and cuDNN to accelerate deep learning computations and reduce training times.

16. Understanding of complete AI/ML project life cycle

17. Understanding of data structures, data modelling and software architecture

18. Good understanding of containerization and experience working with Docker, AKS.

If you're interested then Kindly share your updated resume at 👉 sweety.nathani@worley.com

𝟳 𝗙𝗿𝗲𝗲 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗨𝗽𝗴𝗿𝗮𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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10 Machine Learning Concepts You Must Know

✅ Supervised vs Unsupervised Learning – Understand the foundation of ML tasks
✅ Bias-Variance Tradeoff – Balance underfitting and overfitting
✅ Feature Engineering – The secret sauce to boost model performance
✅ Train-Test Split & Cross-Validation – Evaluate models the right way
✅ Confusion Matrix – Measure model accuracy, precision, recall, and F1
✅ Gradient Descent – The algorithm behind learning in most models
✅ Regularization (L1/L2) – Prevent overfitting by penalizing complexity
✅ Decision Trees & Random Forests – Interpretable and powerful models
✅ Support Vector Machines – Great for classification with clear boundaries
✅ Neural Networks – The foundation of deep learning

React ❤️ for detailed explanation

Google hiring Data Scientist, Product, Google Home

Apply link: https://careers.google.com/jobs/results/122949546446594758-data-scientist/?src=Online/LinkedIn/linkedin_us&utm_source=linkedin&utm_medium=jobposting&utm_campaign=contract

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𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Whether you’re a student, fresher, or professional looking to upskill — Microsoft has dropped a series of completely free courses to get you started.

Learn SQL ,Power BI & More In 2025 

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Data Visualization Tools & Best Practices

1. Power BI:

Purpose: Powerful business analytics tool to visualize and share insights from your data.

Best Practices:

Use simple visuals (avoid overloading with data).

Choose the right chart type (e.g., bar chart for comparisons, line chart for trends).

Use slicers and filters to allow users to explore data interactively.

Keep your color schemes consistent and avoid too many colors.

Use Tooltips for additional context without cluttering the visual.


2. Tableau:

Purpose: Data visualization tool used for creating interactive and shareable dashboards.

Best Practices:

Minimize clutter by reducing non-essential elements (e.g., gridlines, unnecessary labels).

Ensure readability with a clean and intuitive layout.

Use dual-axis charts when comparing two measures in a single visual.

Keep titles and labels concise; avoid redundant information.

Prioritize data integrity (avoid misleading visualizations).


3. Matplotlib & Seaborn (Python):

Purpose: Python libraries for static, animated, and interactive visualizations.

Best Practices:

Use subplots to visualize multiple charts together for comparison.

Keep axes readable with appropriate titles and labels.

Choose appropriate color palettes (e.g., Seaborn has good built-in color schemes).

Annotations can help clarify key points on the chart.

Use log scaling for large numerical ranges to make the data more interpretable.


4. Excel:

Purpose: Widely used tool for simple data analysis and visualization.

Best Practices:

Use pivot charts to summarize data interactively.

Stick to basic chart types (e.g., bar, line, pie) for easy-to-understand visuals.

Use conditional formatting to highlight key trends or outliers.

Label charts clearly (titles, axis names, and legends).

Limit the number of chart elements (don’t overcrowd your chart).


5. Google Data Studio:

Purpose: Free tool for creating dashboards and reports, often integrated with Google products.

Best Practices:

Link to live data sources for automatic updates (e.g., Google Sheets, Google Analytics).

Use dynamic filters to give users control over what data is shown.

Utilize templates for consistent reports and visuals.

Keep reports simple and focused on key metrics.

Design with mobile responsiveness in mind for accessibility.


6. Best Practices for Data Visualization:

Clarity over complexity: Simplify your visuals, removing unnecessary elements.

Choose the right chart: Select charts that best represent the data (e.g., bar for comparisons, line for trends).

Tell a story: Your visual should communicate a clear message or insight.

Consistency in design: Maintain a consistent style for fonts, colors, and layout across all visuals.

Be mindful of colorblindness: Use color schemes that are accessible to all viewers.

Provide context: Include clear titles, labels, and legends for better understanding.

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Hope it helps :)

NetApp hiring Machine Learning Engineer

https://careers.netapp.com/job/-/-/27600/79640994048?jobPipeline=limtedlistings