🔗 Roadmap to master Machine Learning
🥰1
🔗 Roadmap to master Machine Learning
👍4❤1🥰1
🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
❤2👍1🥰1
10 Free Machine Learning Books For 2025
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
📘 1. Foundations of Machine Learning
Build a solid theoretical base before diving into machine learning algorithms.
🔘 Click Here
📙 2. Practical Machine Learning: A Beginner's Guide with Ethical Insights
Learn to implement ML with a focus on responsible and ethical AI.
🔘 Open Book
📗 3. Mathematics for Machine Learning
Master the core math concepts that power machine learning algorithms.
🔘 Click Here
📕 4. Algorithms for Decision Making
Use machine learning to make smarter decisions in complex environments.
🔘 Open Book
📘 5. Learning to Quantify
Dive into the niche field of quantification and its real-world impact.
🔘 Click Here
📙 6. Gradient Expectations
Explore predictive neural networks inspired by the mammalian brain.
🔘 Open Book
📗 7. Reinforcement Learning: An Introduction
A comprehensive intro to RL, from theory to practical applications.
🔘 Click Here
📕 8. Interpretable Machine Learning
Understand how to make machine learning models transparent and trustworthy.
🔘 Open Book
📘 9. Fairness and Machine Learning
Tackle bias and ensure fairness in AI and ML model outputs.
🔘 Click Here
📙 10. Machine Learning in Production
Learn how to deploy ML models successfully into real-world systems.
🔘 Open Book
Like for more ❤️
👍7❤2🥰1
7 Powerful AI Project Ideas to Build Your Portfolio
✅ AI Chatbot – Create a custom chatbot using NLP libraries like spaCy, Rasa, or GPT API
✅ Fake News Detector – Classify real vs fake news using Natural Language Processing and machine learning
✅ Image Classifier – Build a CNN to identify objects (e.g., cats vs dogs, handwritten digits)
✅ Resume Screener – Automate shortlisting candidates using keyword extraction and scoring logic
✅ Text Summarizer – Generate short summaries from long documents using Transformer models
✅ AI-Powered Recommendation System – Suggest products, movies, or courses based on user preferences
✅ Voice Assistant Clone – Build a basic version of Alexa or Siri with speech recognition and response generation
These projects are not just for learning—they’ll also impress recruiters!
#ai #projects
✅ AI Chatbot – Create a custom chatbot using NLP libraries like spaCy, Rasa, or GPT API
✅ Fake News Detector – Classify real vs fake news using Natural Language Processing and machine learning
✅ Image Classifier – Build a CNN to identify objects (e.g., cats vs dogs, handwritten digits)
✅ Resume Screener – Automate shortlisting candidates using keyword extraction and scoring logic
✅ Text Summarizer – Generate short summaries from long documents using Transformer models
✅ AI-Powered Recommendation System – Suggest products, movies, or courses based on user preferences
✅ Voice Assistant Clone – Build a basic version of Alexa or Siri with speech recognition and response generation
These projects are not just for learning—they’ll also impress recruiters!
#ai #projects
👍5❤1
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽 𝘁𝗼 𝘀𝗵𝗮𝗽𝗲 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿: 👇
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean noscripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You can’t call yourself a data scientist if you don’t understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problems—don’t just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁.
𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
Hope this helps you 😊
-> 1. Learn the Language of Data
Start with Python or R. Learn how to write clean noscripts, automate tasks, and manipulate data like a pro.
-> 2. Master Data Handling
Use Pandas, NumPy, and SQL. These are your weapons for data cleaning, transformation, and querying.
Garbage in = Garbage out. Always clean your data.
-> 3. Nail the Basics of Statistics & Probability
You can’t call yourself a data scientist if you don’t understand distributions, p-values, confidence intervals, and hypothesis testing.
-> 4. Exploratory Data Analysis (EDA)
Visualize the story behind the numbers with Matplotlib, Seaborn, and Plotly.
EDA is how you uncover hidden gold.
-> 5. Learn Machine Learning the Right Way
Start simple:
Linear Regression
Logistic Regression
Decision Trees
Then level up with Random Forest, XGBoost, and Neural Networks.
-> 6. Build Real Projects
Kaggle, personal projects, domain-specific problems—don’t just learn, apply.
Make a portfolio that speaks louder than your resume.
-> 7. Learn Deployment (Optional but Powerful)
Use Flask, Streamlit, or FastAPI to deploy your models.
Turn models into real-world applications.
-> 8. Sharpen Soft Skills
Storytelling, communication, and business acumen are just as important as technical skills.
Explain your insights like a leader.
𝗬𝗼𝘂 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁.
𝗬𝗼𝘂 𝗷𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁.
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
Hope this helps you 😊
🥰2❤1👍1
What's the ONE skill you absolutely NEED to master in 2025 to stay ahead of the curve?
🤔 The latest video dives deep into the MOST in-demand skill this year.
Watch Now: https://youtu.be/GuQHC2_pPxc?feature=shared
And trust me, you won't want to miss this!
Register Now: https://surl.li/bbkbvd
🤔 The latest video dives deep into the MOST in-demand skill this year.
Watch Now: https://youtu.be/GuQHC2_pPxc?feature=shared
And trust me, you won't want to miss this!
Register Now: https://surl.li/bbkbvd
👍2❤1🥰1🤣1
For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content
ENJOY LEARNING 👍👍
1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.
2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.
3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://news.1rj.ru/str/datasciencefun
Like if you need similar content
ENJOY LEARNING 👍👍
👍4❤1
Probability for Data Science
❤2👍1🥰1