Forwarded from Generative AI
𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵! 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍
If you’re dreaming of starting a high-paying data career or switching into the booming tech industry, Google just made it a whole lot easier — and it’s completely FREE👨💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4cMx2h2
You’ll get access to hands-on labs, real datasets, and industry-grade training created directly by Google’s own experts💻
If you’re dreaming of starting a high-paying data career or switching into the booming tech industry, Google just made it a whole lot easier — and it’s completely FREE👨💻
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4cMx2h2
You’ll get access to hands-on labs, real datasets, and industry-grade training created directly by Google’s own experts💻
👍2
Please go through this top 5 SQL projects with Datasets that you can practice and can add in your resume
🚀1. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
🚀2. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
📌3. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
🚀4. Inventory Management:
(https://www.kaggle.com/code/govindji/inventory-management)
🚀 5. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.
Hope this piece of information helps you
🚀1. Web Analytics:
(https://www.kaggle.com/zynicide/wine-reviews)
🚀2. Healthcare Data Analysis:
(https://www.kaggle.com/cdc/mortality)
📌3. E-commerce Analysis:
(https://www.kaggle.com/olistbr/brazilian-ecommerce)
🚀4. Inventory Management:
(https://www.kaggle.com/code/govindji/inventory-management)
🚀 5. Analysis of Sales Data:
(https://www.kaggle.com/kyanyoga/sample-sales-data)
Small suggestion from my side for non tech students: kindly pick those datasets which you like the subject in general, that way you will be more excited to practice it, instead of just doing it for the sake of resume, you will learn SQL more passionately, since it’s a programming language try to make it more exciting for yourself.
Hope this piece of information helps you
👍2
𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘😍
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?👨💻
Here’s the truth: YouTube is packed with goldmine content, and the best part — it’s all 100% FREE🔥
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4cL3SyM
🚀 If You’re Serious About Data Analytics, You Can’t Sleep on These YouTube Channels!
Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?👨💻
Here’s the truth: YouTube is packed with goldmine content, and the best part — it’s all 100% FREE🔥
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4cL3SyM
🚀 If You’re Serious About Data Analytics, You Can’t Sleep on These YouTube Channels!
👍1
Forwarded from Artificial Intelligence
𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗢𝗻 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘😍
Want to know how top companies handle massive amounts of data without losing track? 📊
TCS is offering a FREE beginner-friendly course on Master Data Management, and yes—it comes with a certificate! 🎓
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jGFBw0
Just click and start learning!✅️
Want to know how top companies handle massive amounts of data without losing track? 📊
TCS is offering a FREE beginner-friendly course on Master Data Management, and yes—it comes with a certificate! 🎓
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jGFBw0
Just click and start learning!✅️
👍1
🚀 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀? 𝗙𝗼𝗹𝗹𝗼𝘄 𝗧𝗵𝗶𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽! 🚀
Data Science interviews can be daunting, but with the right approach, you can ace them! If you're feeling overwhelmed, here's a roadmap to guide you through the process and help you succeed:
🔍 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀:
Master fundamental concepts like statistics, linear algebra, and probability. These are crucial for tackling both theoretical and practical questions.
💻 𝟮. 𝗪𝗼𝗿𝗸 𝗼𝗻 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀:
Build a strong portfolio by solving real-world problems. Kaggle competitions, open datasets, and personal projects are great ways to gain hands-on experience.
🧠 𝟯. 𝗦𝗵𝗮𝗿𝗽𝗲𝗻 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗸𝗶𝗹𝗹𝘀:
Coding is key in Data Science! Practice on platforms like LeetCode, HackerRank, or Codewars to boost your problem-solving ability and efficiency. Be comfortable with Python, SQL, and essential libraries.
📊 𝟰. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴:
A significant portion of Data Science work revolves around cleaning and preparing data. Make sure you're comfortable with handling missing data, outliers, and feature engineering.
📚 𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀:
From decision trees to neural networks, ensure you understand how different models work and when to apply them. Know their strengths, weaknesses, and the mathematical principles behind them.
💬 𝟲. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀:
Being able to explain complex concepts in a simple way is essential, especially when communicating with non-technical stakeholders. Practice explaining your findings and solutions clearly.
🔄 𝟳. 𝗠𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸:
Practice mock interviews with peers or mentors. Constructive feedback will help you identify areas of improvement and build confidence.
📈 𝟴. 𝗞𝗲𝗲𝗽 𝗨𝗽 𝗪𝗶𝘁𝗵 𝗧𝗿𝗲𝗻𝗱𝘀:
Data Science is a fast-evolving field! Stay updated on the latest techniques, tools, and industry trends to remain competitive.
👉 𝗣𝗿𝗼 𝗧𝗶𝗽: Be persistent! Rejections are part of the journey, but every experience teaches you something new.
Data Science interviews can be daunting, but with the right approach, you can ace them! If you're feeling overwhelmed, here's a roadmap to guide you through the process and help you succeed:
🔍 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀:
Master fundamental concepts like statistics, linear algebra, and probability. These are crucial for tackling both theoretical and practical questions.
💻 𝟮. 𝗪𝗼𝗿𝗸 𝗼𝗻 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀:
Build a strong portfolio by solving real-world problems. Kaggle competitions, open datasets, and personal projects are great ways to gain hands-on experience.
🧠 𝟯. 𝗦𝗵𝗮𝗿𝗽𝗲𝗻 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗸𝗶𝗹𝗹𝘀:
Coding is key in Data Science! Practice on platforms like LeetCode, HackerRank, or Codewars to boost your problem-solving ability and efficiency. Be comfortable with Python, SQL, and essential libraries.
📊 𝟰. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴:
A significant portion of Data Science work revolves around cleaning and preparing data. Make sure you're comfortable with handling missing data, outliers, and feature engineering.
📚 𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀:
From decision trees to neural networks, ensure you understand how different models work and when to apply them. Know their strengths, weaknesses, and the mathematical principles behind them.
💬 𝟲. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀:
Being able to explain complex concepts in a simple way is essential, especially when communicating with non-technical stakeholders. Practice explaining your findings and solutions clearly.
🔄 𝟳. 𝗠𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸:
Practice mock interviews with peers or mentors. Constructive feedback will help you identify areas of improvement and build confidence.
📈 𝟴. 𝗞𝗲𝗲𝗽 𝗨𝗽 𝗪𝗶𝘁𝗵 𝗧𝗿𝗲𝗻𝗱𝘀:
Data Science is a fast-evolving field! Stay updated on the latest techniques, tools, and industry trends to remain competitive.
👉 𝗣𝗿𝗼 𝗧𝗶𝗽: Be persistent! Rejections are part of the journey, but every experience teaches you something new.
Many people still aren't fully utilizing the power of Telegram.
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started 👇👇
Latest Jobs & Internships: https://news.1rj.ru/str/getjobss
Jobs Preparation Resources:
https://news.1rj.ru/str/jobinterviewsprep
Web Development Jobs:
https://news.1rj.ru/str/webdeveloperjob
Data Science Jobs:
https://news.1rj.ru/str/datasciencej
Interview Tips:
https://news.1rj.ru/str/Interview_Jobs
Data Analyst Jobs:
https://news.1rj.ru/str/jobs_SQL
AI Jobs:
https://news.1rj.ru/str/AIjobz
Remote Jobs:
https://news.1rj.ru/str/jobs_us_uk
FAANG Jobs:
https://news.1rj.ru/str/FAANGJob
Software Developer Jobs: https://news.1rj.ru/str/internshiptojobs
If you found this helpful, don’t forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING👍👍
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started 👇👇
Latest Jobs & Internships: https://news.1rj.ru/str/getjobss
Jobs Preparation Resources:
https://news.1rj.ru/str/jobinterviewsprep
Web Development Jobs:
https://news.1rj.ru/str/webdeveloperjob
Data Science Jobs:
https://news.1rj.ru/str/datasciencej
Interview Tips:
https://news.1rj.ru/str/Interview_Jobs
Data Analyst Jobs:
https://news.1rj.ru/str/jobs_SQL
AI Jobs:
https://news.1rj.ru/str/AIjobz
Remote Jobs:
https://news.1rj.ru/str/jobs_us_uk
FAANG Jobs:
https://news.1rj.ru/str/FAANGJob
Software Developer Jobs: https://news.1rj.ru/str/internshiptojobs
If you found this helpful, don’t forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING👍👍
👍1
Forwarded from Artificial Intelligence
𝟱 𝗙𝗿𝗲𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗡𝗲𝗲𝗱𝗲𝗱!)😍
If you’re serious about starting your tech journey, Python is one of the best languages to master👨💻👨🎓
I’ve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects — absolutely FREE🔥
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4lOVqmb
Start today, and you’ll thank yourself tomorrow.✅️
If you’re serious about starting your tech journey, Python is one of the best languages to master👨💻👨🎓
I’ve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects — absolutely FREE🔥
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4lOVqmb
Start today, and you’ll thank yourself tomorrow.✅️
👍1
Machine learning powers so many things around us – from recommendation systems to self-driving cars!
But understanding the different types of algorithms can be tricky.
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.
ENJOY LEARNING 👍👍
But understanding the different types of algorithms can be tricky.
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.
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