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| 2 | 7 Steps of the Machine Learning Process
Data Collection: The process of extracting raw datasets for the machine learning task. This data can come from a variety of places, ranging from open-source online resources to paid crowdsourcing. The first step of the machine learning process is arguably the most important. If the data you collect is poor quality or irrelevant, then the model you train will be poor quality as well.
Data Processing and Preparation: Once you’ve gathered the relevant data, you need to process it and make sure that it is in a usable format for training a machine learning model. This includes handling missing data, dealing with outliers, etc.
Feature Engineering: Once you’ve collected and processed your dataset, you will likely need to transform some of the features (and sometimes even drop some features) in order to optimize how well a model can be trained on the data.
Model Selection: Based on the dataset, you will choose which model architecture to use. This is one of the main tasks of industry engineers. Rather than attempting to come up with a completely novel model architecture, most tasks can be thoroughly performed with an existing architecture (or combination of model architectures).
Model Training and Data Pipeline: After selecting the model architecture, you will create a data pipeline for training the model. This means creating a continuous stream of batched data observations to efficiently train the model. Since training can take a long time, you want your data pipeline to be as efficient as possible.
Model Validation: After training the model for a sufficient amount of time, you will need to validate the model’s performance on a held-out portion of the overall dataset. This data needs to come from the same underlying distribution as the training dataset, but needs to be different data that the model has not seen before.
Model Persistence: Finally, after training and validating the model’s performance, you need to be able to properly save the model weights and possibly push the model to production. This means setting up a process with which new users can easily use your pre-trained model to make predictions. | 539 |
| 3 | ✅ Deep Learning Models You Should Know 🧠📚
1️⃣ Feedforward Neural Networks (FNN)
– Basic neural networks for structured/tabular data
– Example: Classification or regression on tabular datasets
2️⃣ Convolutional Neural Networks (CNN)
– Specialized for image and spatial data
– Example: Image classification, object detection
3️⃣ Recurrent Neural Networks (RNN)
– Processes sequential data
– Example: Time series forecasting, text generation
4️⃣ Long Short-Term Memory (LSTM)
– A type of RNN for long-range dependencies
– Example: Stock price prediction, language modeling
5️⃣ Gated Recurrent Unit (GRU)
– Lightweight alternative to LSTM
– Example: Real-time NLP applications
6️⃣ Autoencoders
– Unsupervised learning for feature extraction & denoising
– Example: Anomaly detection, noise reduction
7️⃣ Generative Adversarial Networks (GANs)
– Generates synthetic data by pitting two networks against each other
– Example: Deepfakes, art generation, image synthesis
8️⃣ Transformer Models
– State-of-the-art for NLP and beyond
– Example: Chatbots, translation (BERT, GPT)
💬 Tap ❤️ for more! | 1 253 |
| 4 |  Your Data Science degree just got an AI update.
Yeah.
Things are moving fast.
Python. SQL. Machine Learning. Deep Learning. MLOps.
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| 5 | Honeywell is hiring Data Scientist 🚀
Experience : 0-1 Year
Location : Bangalore
Apply link : https://www.linkedin.com/jobs/view/4445679023/ | 1 230 |
| 6 | 📊 Data Science Roadmap 🚀
📂 Start Here
∟📂 What is Data Science & Why It Matters?
∟📂 Roles (Data Analyst, Data Scientist, ML Engineer)
∟📂 Setting Up Environment (Python, Jupyter Notebook)
📂 Python for Data Science
∟📂 Python Basics (Variables, Loops, Functions)
∟📂 NumPy for Numerical Computing
∟📂 Pandas for Data Analysis
📂 Data Cleaning & Preparation
∟📂 Handling Missing Values
∟📂 Data Transformation
∟📂 Feature Engineering
📂 Exploratory Data Analysis (EDA)
∟📂 Descriptive Statistics
∟📂 Data Visualization (Matplotlib, Seaborn)
∟📂 Finding Patterns & Insights
📂 Statistics & Probability
∟📂 Mean, Median, Mode, Variance
∟📂 Probability Basics
∟📂 Hypothesis Testing
📂 Machine Learning Basics
∟📂 Supervised Learning (Regression, Classification)
∟📂 Unsupervised Learning (Clustering)
∟📂 Model Evaluation (Accuracy, Precision, Recall)
📂 Machine Learning Algorithms
∟📂 Linear Regression
∟📂 Decision Trees & Random Forest
∟📂 K-Means Clustering
📂 Model Building & Deployment
∟📂 Train-Test Split
∟📂 Cross Validation
∟📂 Deploy Models (Flask / FastAPI)
📂 Big Data & Tools
∟📂 SQL for Data Handling
∟📂 Introduction to Big Data (Hadoop, Spark)
∟📂 Version Control (Git & GitHub)
📂 Practice Projects
∟📌 House Price Prediction
∟📌 Customer Segmentation
∟📌 Sales Forecasting Model
📂 ✅ Move to Next Level
∟📂 Deep Learning (Neural Networks, TensorFlow, PyTorch)
∟📂 NLP (Text Analysis, Chatbots)
∟📂 MLOps & Model Optimization
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React "❤️" for more! 🚀📊 | 1 494 |
| 7 | Lanmea is hiring
Role: SWE and AI Internship
Batch: 2026/27 passouts
Link: https://www.lanmea.com/careers/ai-software-engineering-intern
👉WhatsApp Channel: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
👉Telegram Link: https://t.me/addlist/4q2PYC0pH_VjZDk5
Like for more job opportunities ❤️ | 1 425 |
| 8 |  Want to Build Career in Data Science 📊?
*Join This Free Masterclass*
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| 9 | Two Scholarships Open Now — Airtel (UG Engineering) & IDFC FIRST Bank (MBA)
Two separate scholarship programs are currently accepting applications for Indian students.
Scholarship 1: Bharti Airtel Scholarship 2026-27
Who can apply:
First year UG or 5 year integrated engineering students at Top 50 NIRF ranked institutes
Fields: ECE, Telecom, IT, CS, Data Science, Aerospace, Emerging Tech
Indian citizen and resident
Family income under ₹8.5 lakh per annum
What you get: 100% tuition, hostel and mess fees, plus a free laptop (first year only, one time)
Deadline: July 31, 2026 Apply here: https://bhartiairtelfoundation.org/bharti-airtel-scholarship
Scholarship 2: IDFC FIRST Bank MBA Scholarship 2026-28
Who can apply:
First year students of a 2 year full time MBA/PGDM program at listed eligible colleges
Family income under ₹6 lakh per annum
Age 35 or below on date of application
Valid mobile number linked to Aadhaar
What you get: ₹2 lakh total over 2 years (₹1 lakh per year) toward MBA tuition fees only. Does not cover hostel, mess, books, or travel
Deadline: August 8, 2026 Apply here: https://www.idfcfirstbank.com/csr-activities/educational-initiatives/mba-scholarship
Note: IDFC FIRST Bank does not charge any application fee at any stage.
If you need more such type of content then do let me know by responding to this message. 😊 | 1 363 |
| 10 | We’re looking for an experienced Data Scientist to support a pricing-strategy project.
📍 Berlin — 2 days per week in the office
⏳ 3–4-month contract
📅 Start: ASAP
🤝 Direct contract with the client
You’ll need strong Python and SQL skills, experience building predictive models and simulations, and the ability to explain complex insights clearly. Experience in fintech, insurance, pricing, or other complex systems would be ideal.
We’re looking for someone highly analytical, commercially minded, and a great communicator.
Interested anyone know someone or share your updated resume
marat@zerotoonesearch.com | 1 570 |
| 11 |  If you’re a student, graduate, or someone looking for a career switch, read this.
Most people spend months watching random YouTube videos and still don’t become job-ready.
Instead, learn in a structured offline classroom.
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| 12 | Here’s a solid 𝗕𝗘𝗛𝗔𝗩𝗜𝗢𝗥𝗔𝗟 𝗥𝗢𝗨𝗡𝗗 𝗧𝗜𝗣 to boost your chances to nail that job offer!
Technical skills might get you through initial rounds, but behavioral rounds are where many stumble — especially with senior managers who really want to know if you fit the team.
Here’s how to ace it:
1️⃣ When HR shares your interviewer's name, hunt for their LinkedIn profile.
2️⃣ Check out their work history and interests to find common ground.
3️⃣ Mention something relevant during the chat — it shows you’ve done your homework and builds rapport.
4️⃣ Remember, this round is two-way: they’re checking if you suit their culture, and you’re seeing if they suit your career goals.
5️⃣ So, ask smart questions about the role and company culture — it proves you’re genuinely interested.
💡 𝗣𝗿𝗼 𝘁𝗶𝗽: Stay polite but confident; senior leaders love that mix! | 1 317 |
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| 14 | 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. | 1 475 |
| 15 | Flipkart is hiring Data Scientist 🚀📈
Location : Bangalore
Apply link : https://www.linkedin.com/jobs/view/4436043289/ | 1 479 |
| 16 | If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
USEFUL RESOURCES TO LEARN MACHINE LEARNING
👇👇
Intro to ML by MIT Free Course
https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about
Machine Learning for Everyone FREE BOOK
https://buildmedia.readthedocs.org/media/pdf/pymbook/latest/pymbook.pdf
ML Crash Course by Google
https://developers.google.com/machine-learning/crash-course
Advanced Machine Learning with Python Github
https://github.com/PacktPublishing/Advanced-Machine-Learning-with-Python
Practical Machine Learning Tools and Techniques Free Book
https://vk.com/doc10903696_437487078?hash=674d2f82c486ac525b&dl=ed6dd98cd9d60a642b
Python Machine Learning for beginners
https://t.me/datasciencefun/1177?single
https://topmate.io/coding/914624
All the best 👍👍 | 1 289 |
| 17 |  📢 Advertising in this channel
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| 18 | 🎯 7 YouTube Courses = 4 Years Degree👇
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| 19 |  Want to Build Career in Data Science 📊?
Join This Free Masterclass
📅 Date: July 03, 2026
⏰ Time: 7:00 PM IST
🌐 Language: English
📚 Learn:
• ML Foundations
• Deep Learning Basics
• Real-world AI Applications
• Live Q&A + Get Participation Certificate
👥 For:
Freshers | Working Professionals | Career Switchers
🤳🏼 Register Here:
https://rebrand.ly/Data-science-webinar | 1 166 |
| 20 | Top companies currently hiring data analysts
Based on the current job market, here are the top companies hiring data analysts:
## Top Tech Companies
- Meta: Investing heavily in AI with significant GPU investments
- Amazon: Offers diverse data analyst roles with complex responsibilities
- Google (Alphabet): Leverages massive data ecosystems
- JP Morgan Chase & Co.: Strong focus on data-driven banking transformation
## Specialized Data Analytics Firms
- Tiger Analytics: Specializes in AI/ML solutions
- SG Analytics: Provides data-driven insights
- Monte Carlo Data: Focuses on data observability
- CB Insights: Excels in market intelligence
## Emerging Opportunities
Companies like Samsara, ScienceSoft, and Forage are also actively recruiting data analysts, offering competitive salaries ranging from $85,000 to $207,000 annually.
Share with credits: https://t.me/sqlspecialist
Hope it helps :) | 1 307 |
