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.

Best Data Science & Machine Learning Resources: https://topmate.io/learning_resources/1406977

All the best 👍👍

Machine Learning Resources 👇
https://t.me/mlresourcestp

REMOTE

Remote Ruby Engineer Job at Silverfin

Perks:
- A salary range of €99,000 - €134,000 a year
- Flexible working hours and work 100% remotely
- A €1200 yearly budget for conferences, courses, workshops or other expenses that will improve your skills
among others

What will you work on?
* Building and maintaining integrations with accountancy software packages and APIs
* Improving and expanding our on-premise Ruby CLI/service which runs on hundreds of our customer’s systems
* Work on user facing functionalities
* Help with discovery and delivery of a solution for user or business problems

Apply Here:
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7 SIMPLE RULES FOR LONG-TERM FITNESS SUCCESS

1. Train at least 3x a week – Consistency beats intensity.
2. Prioritize compound movements – Squats, deadlifts, and presses build full-body strength.
3. Track your progress – What gets measured, improves.
4. Fuel your body, not your cravings – Eat for performance, not just taste.
5. Sleep like it matters – Because it does.
6. Don’t chase quick fixes – Focus on sustainable changes.
7. Stay patient and enjoy the process – Results take time, effort, and mindset.

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💠 6 Software Architectural Patterns You Must Know

Choosing the right software architecture pattern is essential for solving problems efficiently.

1 - Layered Architecture

Each layer plays a distinct and clear role within the application context.

Great for applications that need to be built quickly. On the downside, source code can become unorganized if proper rules aren’t followed

2 - Microservices Architecture
Break down a large system into smaller and more manageable components.

Systems built with microservices architecture are fault tolerant. Also, each component can be scaled individually. On the downside, it might increase the complexity of the application.

3 - Event-Driven Architecture
Services talk to each other by emitting events that other services may or may not consume.

This style promotes loose coupling between components. However, testing individual components becomes challenging

4 - Client-Server Architecture
It comprises two main components - clients and servers communicating over a network.

Great for real-time services. However, servers can become a single point of failure.

5 - Plugin-based Architecture
This pattern consists of two types of components - a core system and plugins. The plugin modules are independent components providing a specialized functionality.

Great for applications that have to be expanded over time like IDEs. However, changing the core is difficult.

6 - Hexagonal Architecture
This pattern creates an abstraction layer that protects the core of an application and isolates it from external integrations for better modularity. Also known as ports and adapters architecture.

On the downside, this pattern can lead to increased development time and learning curve.

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Someone's cooked here 🥲

Lucky people get opportunities,

Brave people create opportunities,

But

Real winners are those who convert Their problems into opportunities.

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The next half-decade will redefine what it means to be a developer. Continuous learning isn't a luxury, it's the default.

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What makes an app actually work?

1) Sign up & log in (auth)
2) Save and load data (database)
3) Accept money (payments)
4) Send updates (emails & notifications)
5) Fast and clean UI (frontend)
6) Logic runs smooth (backend)
7) Track usage (analytics)
8) Safe & stable (security)

It’s not just code.
It’s the full system behind it.

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📚 Cookies 🍪 vs Cache 🗂 📚

Cookies 🍪:
- Purpose: Stores information about your interaction with a website.
- Examples: Session ID, IP address, location, login details.
- Storage: Linked to the server and sent with requests to websites. 🌐
- Space: Takes up very little storage. 📂
- Expiration: Often has an expiration date (e.g., 30 days). ⏳
- Usage: Tracks preferences for smoother user experience (e.g., PH4N745M saving login). ✅
- Tracking: Used for personalized ads and tracking user activity. 👀
- Deletion: Requires you to log in or reset preferences next time. 🔄

Cache 🗂:
- Purpose: Stores website assets for faster load times.
- Examples: Images, videos, HTML files, JavaScript. 🖼
- Storage: Stored on your device only. 💻
- Space: Takes up significant storage space. 📦
- Usage: Reduces bandwidth usage and improves speed. ⚡️
- Tracking: Can still track browsing behavior. 🔍
- Deletion: Re-downloads files on the next visit, affecting speed temporarily. 🌀

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😹Hey Seniors and Juniors

No is a full sentence.

🔗 Instill Core is a universal tool for working with unstructured data.

This open-source project offers a comprehensive solution for ETL processing, data preparation for AI, and deployment of LLM models. The platform combines document, image, and video processing into a single workflow, which is especially valuable for RAG scenarios and building AI pipelines.

Instill Core can be easily integrated into existing systems via the Python/TypeScript SDK or CLI. Local execution is possible via Docker, and ready-made recipes allow you to quickly deploy PDF parsing, web scraping, or image segmentation.

🔗 GitHub (https://github.com/instill-ai/instill-core)

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bruh has proved that words have no power🙂