ShareChat is hiring Product Analyst Intern 🚀

Qualification : Bachelor's degree
Experience : Freshers / Experienced
Location : Bangalore

Apply link : Check out this job at ShareChat: https://www.linkedin.com/jobs/view/4197858780

L&T Finance hiring AI/ML Scientist

https://www.linkedin.com/jobs/view/4178430877

Canva hiring Staff Data Scientist

https://www.lifeatcanva.com/en/jobs/6000000000324387/staff-data-scientist-marketing-research-data-remote-across-anz/

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

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

All the best 👍👍

Meesho is hiring Data Scientist 🚀

Experience : 1-2 Year
Location : Bangalore

Apply link : https://meesho.io/jobs/data-scientist--i?id=81b0947f-5a1e-4a51-93d5-bd63d954cf75

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

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

All the best 👍👍

We are looking to hire AI/ML Engineers.
AI/ML Engineer
Full-time position with Vivid Edge Corp
Bangalore - Onsite (Hybrid)
If interested please share profile on my e-mail id : vijay.anand@vivid-edge.com

Citi hiring Data Science Engineer

https://www.linkedin.com/jobs/view/4162414661

Planet is hiring Machine Learning Engineer (Remote)
https://job-boards.greenhouse.io/planetlabs/jobs/6670759

EY is hiring!
Position: Associate - Power BI/ SQL
Qualifications: Bachelor's degree
Salary: 4 - 10 LPA (Expected)
Experience: Freshers/ Experienced
Location: PAN India

📌Apply Now: https://careers.ey.com/ey/job/Kochi-Associate-BI-KL-682303/1187886701/

Larsen & Toubro hiring: https://www.linkedin.com/jobs/view/4085627286

Data Scientist Role

Tech Talent Hub hiring Data Scientist

Location : Dubai, United Arab Emirates

https://www.careers-page.com/tech-talent-hub-3/job/L79WWR8X?utm_medium=free_job_board&utm_source=linkedin

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

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

All the best 👍👍

Divami - Design & AI-Led Product Engineering is hiring Software Engineer

For 2021, 2022 gards
Location: Hyderabad

https://www.linkedin.com/jobs/view/4200591954

If you're into deep learning, then you know that students usually take one of the two paths:

- Computer vision
- Natural language processing (NLP)

If you're into NLP, here are 5 fundamental concepts you should know:
👇👇
https://t.me/generativeai_gpt/7

Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 

These free, Microsoft-backed courses are a game-changer!

With these resources, you’ll gain the skills and confidence needed to shine in the data analytics world—all without spending a penny.

𝐋𝐢𝐧𝐤 👇:- 

https://pdlink.in/4jpmI0I

Enroll For FREE & Get Certified🎓

I often get asked- what's the BEST Certification for #datascience or #machinelearning?

👉My answer is: none

The reality is that certification don't matter for data science.

This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.

The problems are challenging, the data is messy and numerous techniques are used.

So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.

👉 Instead, grab a dataset and start playing with it.

👉 Start applying what you know and trying to solve interesting problems, learn something new every day.

👉 Here are few places to grab datasets to get you started



Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/

𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

Want to turn raw data into stunning visual stories?📊

Here are 6 FREE Power BI courses that’ll take you from beginner to pro—without spending a single rupee💰

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4cwsGL2

Enjoy Learning ✅️

CGI is hiring Data Engineer

https://cgi.njoyn.com/corp/xweb/xweb.asp?NTKN=c&clid=21001&Page=JobDetails&Jobid=J0125-0898&BRID=1197405&lang=1

Airtel Machine Learning Engineer

https://in.linkedin.com/jobs/view/machine-learning-engineer-at-airtel-4193434706

🔗 Harvard study shows AI has effectively become equal to having a second human teammate

Two key points from the paper:

- In an experiment with 776 professionals at Procter & Gamble, individuals using AI performed about the same as teams without AI

- Teams using AI performed much better, often creating the best solutions. they also worked 12–16% faster and gave longer, more detailed answers than those without AI

This indicates that AI has begun to match or replace human collaboration