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 👇:- 

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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👇:-

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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

❗️CGI Off Campus Drive 2025 Hiring Freshers For Associate Software Engineer Role | 4-8 LPA❗

👨‍💻Designation : Associate Software Engineer
🎓Eligibility : BE/BTech
🎖Batch : 2023 / 2024
💰Salary : INR 4-8 LPA

⭕️ Apply Fast : https://cgi.njoyn.com/corp/xweb/xweb.asp?CLID=21001&page=jobdetails&JobID=J0125-1910&lang=1

Codvo hiring AI Senior Technical Lead

Apply link: https://www.linkedin.com/jobs/view/4202850511

Easy Apply through LinkedIn 👆

6⃣k completed, thanks everyone for the love & support ❤️

Data Scientist Job at Impetus: https://www.linkedin.com/jobs/view/4203764847

Actimise hiring Data Scientist

Location: Pune, India

Apply link: https://job-boards.eu.greenhouse.io/nice/jobs/4567868101?gh_jid=4567868101&gh_src=ff5c4b52teu

👉WhatsApp Channel: https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

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

All the best 👍👍

𝗜𝗻𝗳𝗼𝘀𝘆𝘀 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resume—at no cost.

Whether you’re a student, graduate, or working professional, this platform has something valuable for everyone.

𝐋𝐢𝐧𝐤 👇:-

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Enroll For FREE & Get Certified 🎓

©How fresher can get a job as a data scientist?©

1. Education: Obtain a degree in a relevant field such as computer science, statistics, mathematics, or data science. Consider pursuing additional certifications or specialized courses in data science to enhance your skills.

2. Build a strong foundation: Develop a strong understanding of key concepts in data science such as statistics, machine learning, programming languages (such as Python or R), and data visualization.

3. Hands-on experience: Gain practical experience by working on projects, participating in hackathons, or internships. Building a portfolio of projects showcasing your data science skills can be beneficial when applying for jobs.

4. Networking: Attend industry events, conferences, and meetups to network with professionals in the field. Networking can help you learn about job opportunities and make valuable connections.

5. Apply for entry-level positions: Look for entry-level positions such as data analyst, research assistant, or junior data scientist roles to gain experience and start building your career in data science.

6. Prepare for interviews: Practice common data science interview questions, showcase your problem-solving skills, and be prepared to discuss your projects and experiences related to data science.

7. Continuous learning: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends, tools, and techniques. Consider taking online courses, attending workshops, or joining professional organizations to continue learning and growing in the field.

ML Engineer - Jaipur
Product Organisation - Jaipur, Rajasthan (Hybrid)

https://peak.bamboohr.com/careers/370

𝟱 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁, 𝗔𝗪𝗦, 𝗜𝗕𝗠, 𝗖𝗶𝘀𝗰𝗼, 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱. 😍

- Python
- Artificial Intelligence,
- Cybersecurity
- Cloud Computing, and
- Machine Learning

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/3E2wYNr

Enroll For FREE & Get Certified 🎓

Most Demanding Data Analytics Skills!

↳ Dive into the essential skills and tools that are shaping the future of data analytics. From SQL and Python to Tableau and PowerBI, discover which technologies are crucial for advancing your data analysis capabilities.

↳ Explore the importance of machine learning techniques like linear regression, logistic regression, SVM, decision trees, random forests, K-means, and K-nearest neighbors, and how they can enhance your analytical prowess.

↳ Understand why soft skills such as communication, collaboration, critical thinking, and creativity are just as important as technical skills in the data analytics field.

↳ Get a comprehensive overview of the skills and technologies that can propel your career forward and make you a standout in the competitive world of data analytics.