Hypersonix is hiring Data Science Intern 🚀

Qualification : Bachelor's degree
Experience : Freshers
Location : Remote

Apply link : Check out this job at Hypersonix Inc: https://www.linkedin.com/jobs/view/4145413163

Sprinklr is hiring Machine Learning Associate 🚀

Qualification : Bachelor's degree
Experience : 0-2 Years
Location : Gurgaon

Apply link : https://sprinklr.wd1.myworkdayjobs.com/careers/job/India---Haryana---Gurgaon/Machine-Learning-Associate_110166-JOB?source=LinkedIn

Company Name: Alp Consulting

*Job Title:* Data Analyst
*Category:* Data Analytics
*Freshers / Experience:* 0-3 years
*Location:* Not specified
Apply Link: careers@alpconsulting.in

Anovate is hiring Data Analyst 🚀

Experience : 0-3 Year
Location : Mumbai

Apply link : Check out this job at Anovate: https://www.linkedin.com/jobs/view/4146318465

Rebel Foods is hiring Data Scientist 🚀

Min. Experience : 6 Months
Location : Mumbai

Apply link : Check out this job at Rebel Foods: https://www.linkedin.com/jobs/view/4148281105

Exploratory Data Analysis ( EDA)

Adobe Hiring !!
Role - ML Engineer
Exp -2 year

Link- https://careers.adobe.com/us/en/job/ADOBUSR152721EXTERNALENUS/Machine-Learning-Engineer-2

Average Net Monthly Salary per country

Where is your country at? Is the data correct?

Finance Buddha is hiring Junior Data Analyst 🚀

Experience : 0-1 Year
Location : Bangalore

Apply link : Check out this job at Finance Buddha: https://www.linkedin.com/jobs/view/4151858148

BrainWonders is hiring Data Scientist Intern 🚀 ( Paid )

Experience : Freshers
Location : Mumbai

Apply link : https://jobs.smartrecruiters.com/Brainwonders/744000041931048-data-scientist-intern

𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐧𝐬𝐡𝐢𝐩!
Qualifications: Currently pursuing a Bachelor's Degree in Data Science, Mathematics, Statistics, Computer Science, or related field.
Salary: ₹ 1,07,698 Per Month (Expected)
Batch: 2024/ 2025/ 2026/ 2027
Experience: Freshers
Location: Multiple Locations, India

📌Apply Now: https://jobs.careers.microsoft.com/global/en/share/1808941/?utm_source=Job%20Share&utm_campaign=Copy-job-share

👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226

👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5

Like for more ❤️

10 Things you need to become an AI/ML engineer:

1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models

Dreame Technology is hiring Data Scientist 🚀

Qualification : Bachelor's degree
Experience : 0-5 Years
Location : Remote ( USA )

Apply link : https://app.dover.com/apply/dreametechnology/7b83aad6-235c-4582-98d4-589e65835452?rs=42706078

Cousera Hiring!!
Role - ML Engineer
Exp - 3 year btech , 1 year mtech

https://job-boards.greenhouse.io/coursera/jobs/5439503004?gh_src=6d6e4f994us

Unsiloed AI is hiring for 10x ML Engineers to join our team full-time. Apply below and be part of a fast-moving team solving hard AI problems in document intelligence.

Experience: 1-3 years
CTC: 25-30 lpa (base)
https://app.dover.com/apply/Unsiloed-Ai/368c656d-764b-4eaf-87a7-ea22f665dc1a

YouTube channels to learn Data Analysis

Machine Learning Jobs Remote | Best Guide by Data Simplifier
https://datasimplifier.com/machine-learning-jobs-remote/

🔰 How to become a data scientist in 2025?

👨🏻‍💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.


🔢 Step 1: Strengthen your math and statistics!

✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:

✅ Linear algebra: matrices, vectors, eigenvalues.

🔗 Course: MIT 18.06 Linear Algebra


✅ Calculus: derivative, integral, optimization.

🔗 Course: MIT Single Variable Calculus


✅ Statistics and probability: Bayes' theorem, hypothesis testing.

🔗 Course: Statistics 110

➖➖➖➖➖

🔢 Step 2: Learn to code.

✏️ Learn Python and become proficient in coding. The most important topics you need to master are:

✅ Python: Pandas, NumPy, Matplotlib libraries

🔗 Course: FreeCodeCamp Python Course

✅ SQL language: Join commands, Window functions, query optimization.

🔗 Course: Stanford SQL Course

✅ Data structures and algorithms: arrays, linked lists, trees.

🔗 Course: MIT Introduction to Algorithms

➖➖➖➖➖

🔢 Step 3: Clean and visualize data

✏️ Learn how to process and clean data and then create an engaging story from it!

✅ Data cleaning: Working with missing values ​​and detecting outliers.

🔗 Course: Data Cleaning

✅ Data visualization: Matplotlib, Seaborn, Tableau

🔗 Course: Data Visualization Tutorial

➖➖➖➖➖

🔢 Step 4: Learn Machine Learning

✏️ It's time to enter the exciting world of machine learning! You should know these topics:

✅ Supervised learning: regression, classification.

✅ Unsupervised learning: clustering, PCA, anomaly detection.

✅ Deep learning: neural networks, CNN, RNN


🔗 Course: CS229: Machine Learning

➖➖➖➖➖

🔢 Step 5: Working with Big Data and Cloud Technologies

✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.

✅ Big Data Tools: Hadoop, Spark, Dask

✅ Cloud platforms: AWS, GCP, Azure

🔗 Course: Data Engineering

➖➖➖➖➖

🔢 Step 6: Do real projects!

✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.

✅ Kaggle competitions: solving real-world challenges.

✅ End-to-End projects: data collection, modeling, implementation.

✅ GitHub: Publish your projects on GitHub.

🔗 Platform: Kaggle🔗 Platform: ods.ai

➖➖➖➖➖

🔢 Step 7: Learn MLOps and deploy models

✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.

✅ MLOps training: model versioning, monitoring, model retraining.

✅ Deployment models: Flask, FastAPI, Docker

🔗 Course: Stanford MLOps Course

➖➖➖➖➖

🔢 Step 8: Stay up to date and network

✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.

✅ Read scientific articles: arXiv, Google Scholar

✅ Connect with the data community:

🔗 Site: Papers with code
🔗 Site: AI Research at Google

#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data