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/coding/914624

All the best 👍👍

FREE DATASET BUILDING YOUR PORTFOLIO ⭐

1. Supermarket Sales - https://lnkd.in/e86UpCMv
2.Credit Card Fraud Detection - https://lnkd.in/eFTsZDCW
3. FIFA 22 complete player dataset - https://lnkd.in/eDScdUUM
4. Walmart Store Sales Forecasting - https://lnkd.in/eVT6h-CT
5. Netflix Movies and TV Shows - https://lnkd.in/eZ3cduwK
6.LinkedIn Data Analyst jobs listings - https://lnkd.in/ezqxcmrE
7. Top 50 Fast-Food Chains in USA - https://lnkd.in/esBjf5u4
8. Amazon and Best Buy Electronics - https://lnkd.in/e4fBZvJ3
9. Forecasting Book Sales - https://lnkd.in/eXHN2XsQ
10. Real / Fake Job Posting Prediction - https://lnkd.in/e5SDDW9G

Join for more: https://t.iss.one/DataPortfolio

Hope it helps:)

Machine Learning Roadmap

Roadmap to become Data Scientist

Guys, Big Announcement! 🚀

We've officially hit 3 Lakh subscribers on WhatsApp— and it's time to kick off the next big learning journey together! 🤩

Artificial Intelligence Complete Series — a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!

This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.

Here’s what we’ll cover:

*Week 1: Introduction to AI*

- What is AI? Understanding the basics without the jargon

- Types of AI: Narrow vs. General AI

- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)

- Real-world applications: From Chatbots to Self-Driving Cars 🚗

- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)


*Week 2: Core AI Techniques*

- Supervised vs. Unsupervised Learning

- Understanding Data: The backbone of AI

- Linear Regression: Your first AI algorithm!

- Decision Trees, K-Nearest Neighbors, and Support Vector Machines

- Hands-on project: Building a basic classifier with Python 🐍


*Week 3: Deep Dive into Machine Learning*

- What makes ML different from AI?

- Gradient Descent & Model Optimization

- Evaluating Models: Accuracy, Precision, Recall, and F1-Score

- Hyperparameter Tuning

- Hands-on project: Building a predictive model with real data 📊


*Week 4: Introduction to Neural Networks*

- The fundamentals of neural networks & deep learning

- Understanding how a neural network mimics the human brain 🧠

- Training your first Neural Network with TensorFlow

- Introduction to Backpropagation and Activation Functions

- Hands-on project: Build a simple neural network to recognize images 📸


*Week 5: Advanced AI Concepts*

- Natural Language Processing (NLP): Teach machines to understand text and speech 🗣️

- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)

- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)

- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more

- Hands-on project: Implementing NLP for text classification 📚


*Week 6: Building Real-World AI Applications*

- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection

- Integrating AI with APIs and Web Services

- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects

- Hands-on project: Build a recommendation system like Netflix 🎬


*Week 7: Preparing for AI Careers*

- Common interview questions for AI & ML roles 📝

- Building an AI Portfolio: Showcase your projects

- Understanding AI in Industry: How it’s transforming businesses

- Networking and building your career in AI 🌐


Join our WhatsApp channel to access it for FREE: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y/1031

7 Essential Data Analysis Techniques You Need to Know in 2025

✅ Exploratory Data Analysis (EDA) – Uncover patterns, spot anomalies, and visualize distributions before diving deeper
✅ Time Series Analysis – Analyze trends over time, forecast future values (using ARIMA or Prophet)
✅ Hypothesis Testing – Use statistical tests (T-tests, Chi-square) to validate assumptions and claims
✅ Regression Analysis – Predict continuous variables using linear or non-linear models
✅ Cluster Analysis – Group similar data points using K-means or hierarchical clustering
✅ Dimensionality Reduction – Simplify complex datasets using PCA (Principal Component Analysis)
✅ Classification Algorithms – Predict categorical outcomes with decision trees, random forests, and SVMs

Mastering these will give you the edge in any data analysis role.

Free Resources: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02

Free Programming and Data Analytics Resources 👇👇

✅ Data science and Data Analytics Free Courses by Google

https://developers.google.com/edu/python/introduction

https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field

https://cloud.google.com/data-science?hl=en

https://developers.google.com/machine-learning/crash-course

https://t.iss.one/datasciencefun/1371

🔍 Free Data Analytics Courses by Microsoft

1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/

3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

🤖 Free AI Courses by Microsoft

1. Fundamentals of AI by Microsoft

https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

2. Introduction to AI with python by Harvard.

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

📚 Useful Resources for the Programmers

Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94

Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019

Interactive React Native Resources
https://fullstackopen.com/en/part10

Python for Data Science and ML
https://t.iss.one/datasciencefree/68

Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3

Unity Documentation
https://docs.unity3d.com/Manual/index.html

Advanced Javascript concepts
https://t.iss.one/Programming_experts/72

Oops in Java
https://nptel.ac.in/courses/106105224

Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction

Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76

Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em

Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single

🍻 Free Programming Courses by Microsoft

❯ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/

❯ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

Some common supervised learning algorithms include:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

Some popular unsupervised learning algorithms include:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

Common semi-supervised learning algorithms include:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

Popular reinforcement learning algorithms include:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

Like if you need similar content 😄👍

Hope this helps you 😊