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Machine Learning & Artificial Intelligence | Data Science Free Courses
23 Sept, 19:17
Machine Learning Roadmap
Machine Learning & Artificial Intelligence | Data Science Free Courses
23 Sept, 09:59
SQL & Python Cheatsheet for Beginners ❤️
Machine Learning & Artificial Intelligence | Data Science Free Courses
22 Sept, 16:29
#Ad #AI_Models🔥 GigaChat 3.5 Reasoning [Open-Source]ℹ️ Overview: New LLM that thinks before it answers. Breaks problems into stages, builds plans, checks results, and self-corrects using automated verification.🔗 Source: Hugging Face fp8 | bf16📝 Model Specs:✪ Built on GigaChat 3.5 Ultra with multiple step-by-step reasoning paths✪ Proprietary linear attention for efficient long contexts✪ Token-efficient: 37% fewer tokens than DeepSeek V4 Flash Preview✪ Benchmarks: IFBench 44→77, Natural Plan 64→80, LiveCodeBench v6 56→85Machine Learning & Artificial Intelligence | Data Science Free Courses
22 Sept, 15:46
✅ Programming Languages, Libraries & Tools Every Tech Field Uses 👨💻🚀🧠 DATA SCIENCE & MACHINE LEARNING1. Python → Pandas, NumPy, TensorFlow, PyTorch2. R → ggplot2, dplyr, caret3. SQL → PostgreSQL, MySQL4. Julia → Flux, Pluto🤖 ARTIFICIAL INTELLIGENCE1. Python → Keras, OpenCV, LangChain2. C++ → OpenCV, CUDA3. Java → Deeplearning4jMachine Learning & Artificial Intelligence | Data Science Free Courses
21 Sept, 05:22
𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.📅 Date: 24 September 2026 ⏰ Time: 7:00 PM–9:00 PM IST 🌐 Mode: Online 🎓 Certificate: Available to all attendeesEligibility :- Graduates Passing In 2025 or earlier🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇https://pdlink.in/4xAMeGW⚡ Register now and take your first step towards a successful career in AI!
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20 Sept, 19:46
✅ 🔤 A–Z of Machine LearningA – Artificial Neural Networks Computing systems inspired by the human brain, used for pattern recognition.B – Bagging Ensemble technique that combines multiple models to improve stability and accuracy.C – Cross-Validation Method to evaluate model performance by partitioning data into training and testing sets.D – Decision Trees Models that split data into branches to make predictions or classifications.E – Ensemble Learning Combining multiple models to improve overall prediction power.F – Feature Scaling Techniques like normalization to standardize data for better model performance.Machine Learning & Artificial Intelligence | Data Science Free Courses
17 Sept, 10:26edited
🧠 Skills & Techniques for Data Science, Machine Learning & AI!📊 Core Data Science Skills ▪️ Probability & Statistics – Foundation of Data Insights ▪️ Hypothesis Testing – Validating Assumptions ▪️ Regression Analysis – Predictive Modeling ▪️ A/B Testing – Experimentation for Business Impact ▪️ Data Cleaning – Turning Raw Data into Usable Insights🤖 Machine Learning Techniques ▪️ Linear & Logistic Regression – Predictive Models ▪️ Decision Trees / Random Forest – Classification & Prediction ▪️ K-means / Hierarchical Clustering – Grouping Data ▪️ PCA – Dimensionality Reduction ▪️ Cross-validation – Reliable Model Testing🧠 AI & GenAI Skills ▪️ Prompt Engineering – Getting Best from LLMs ▪️ OpenAI APIs – Building AI-powered Apps ▪️ Hugging Face Transformers – NLP at ScaleMachine Learning & Artificial Intelligence | Data Science Free Courses
16 Sept, 18:25edited
✅ Machine Learning Explained for Beginners 🤖📚📌 Definition:Machine Learning (ML) is a type of artificial intelligence that allows systems to learn from data and make decisions or predictions without being explicitly programmed for every task.1️⃣ How It Works:ML systems are trained on historical data to identify patterns. Once trained, they apply those patterns to new, unseen data.Example: Feed a model emails labeled "spam" or "not spam," and it learns how to filter spam automatically.2️⃣ Types of Machine Learning:a) Supervised Learning• Learns from labeled data (inputs + expected outputs)• Examples: Email classification, price predictionMachine Learning & Artificial Intelligence | Data Science Free Courses
15 Sept, 15:25
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-probability𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚Machine Learning & Artificial Intelligence | Data Science Free Courses
14 Sept, 07:05
Useful Telegram Channels for Free Learning 😄👇Free Courses with CertificateWeb DevelopmentData Science & Machine LearningProgramming booksPython Free CoursesData AnalyticsEthical Hacking & Cyber SecurityEnglish Speaking & CommunicationStock Marketing & Investment BankingMachine Learning & Artificial Intelligence | Data Science Free Courses
14 Sept, 07:04
FREE RESOURCES TO LEARN DATA ENGINEERING 👇👇Big Data and Hadoop Essentials free coursehttps://bit.ly/3rLxbulData Engineer: Prepare Financial Data for ML and Backtesting FREE UDEMY COURSE [4.6 stars out of 5]https://bit.ly/3fGRjLuUnderstanding Data Engineering from Datacamphttps://clnk.in/soLYData Engineering Free Bookshttps://ia600201.us.archive.org/4/items/springer_10.1007-978-1-4419-0176-7/10.1007-978-1-4419-0176-7.pdfMachine Learning & Artificial Intelligence | Data Science Free Courses
13 Sept, 05:26
👉 BEST DATA SCIENCE CHANNELS ON TELEGRAM 👈https://t.me/addlist/8_rRW2scgfRhOTc0TelegramData scienceYou’ve been invited to add the folder “Data science”, which includes 15 chats.Machine Learning & Artificial Intelligence | Data Science Free Courses
12 Sept, 09:30
👉 BEST DATA SCIENCE CHANNELS ON TELEGRAM 👈https://t.me/addlist/8_rRW2scgfRhOTc0TelegramData scienceYou’ve been invited to add the folder “Data science”, which includes 15 chats.Machine Learning & Artificial Intelligence | Data Science Free Courses
12 Sept, 09:28
🔥 10 Useful Resources You Need to Know About1. 🌐 Google Scholar – Find academic papers, research & scholarly articles2. 📚 Project Gutenberg – Thousands of free classic books3. 🎓 Coursera – Online courses from universities & companies4. 💻 GitHub – Explore code, open-source projects & developer resources5. 🧠 Wolfram Alpha – Computational answers for math, science & more6. 📖 Internet Archive – Books, websites, videos & historical resources7. 🧪 PubMed – Search biomedical & life-science research8. 🎓 MIT OpenCourseWare – Free university course materials from MIT9. 📝 Notion – Organize notes, projects, knowledge & study materialsMachine Learning & Artificial Intelligence | Data Science Free Courses
10 Sept, 17:45
✅ Data Science Portfolio Tips 🚀A Data Science portfolio is your proof of skill — it shows recruiters that you don’t just “know” concepts, but you can apply them to solve real problems. Here’s how to build an impressive one:🔹 What to Include in Your Portfolio • 3–5 Real Projects (end-to-end): e.g., data cleaning, EDA, ML modeling, evaluation, and conclusion • ReadMe Files: Clearly explain each project — objectives, steps, and results • Visuals: Add graphs, dashboards, or screenshots • Code + Output: Well-commented Python code + output samples (charts/tables) • Domain Variety: Include projects from healthcare, finance, e-commerce, etc.🔹 Where to Host Your Portfolio • GitHub: Ideal for code, Jupyter Notebooks, version control → Use pinned repo section → Keep repos clean and organized → Add a main README linking to your best work• Notion: Great as a personal portfolio site → LinkMachine Learning & Artificial Intelligence | Data Science Free Courses
6 Sept, 20:20
Machine Learning & Artificial Intelligence | Data Science Free Courses
6 Sept, 08:30
To learn Data Science from basic to advanced levels, you can follow these steps: 🤩🤩⏩ Python Programming:Start with Python, one of the most widely used programming languages in Data Science. Learn variables, data types, loops, functions, object-oriented programming, and file handling. Then become comfortable with libraries such as NumPy, Pandas, Matplotlib, and Seaborn.⏩ Mathematics and Statistics:Build a strong foundation in mathematics and statistics. Learn concepts such as mean, median, variance, standard deviation, probability, distributions, correlation, regression, hypothesis testing, and basic linear algebra.⏩ Data Collection:Learn how to collect data from different sources. Understand CSV and Excel files, databases, APIs, web data, and other data sources. Learn how to work with both structured and unstructured data.⏩ Data Cleaning and Preprocessing:Real-worldMachine Learning & Artificial Intelligence | Data Science Free Courses
2 Sept, 17:46
Top 10 Free Training Courses on AI for Everyone1️⃣ Elements of AI: - Link 2️⃣ Google AI for Everyone : Link 3️⃣ IBM AI Foundations for Everyone:- Link 4️⃣ Harvard University : - Link 5️⃣ AWS Skill Builder :- Link 6️⃣ Deep Learning Fundamentals :- Link 7️⃣ Machine Learning Basics:- Link 8️⃣ TensorFlow Basics:- Link 9️⃣ Keras for Beginners:- Link 🔟 ChatGPT Prompt Engineering for Developers:- LinkMachine Learning & Artificial Intelligence | Data Science Free Courses
31 Aug, 11:08
Hey!I'm Stacy and I bought an ad post here to share 3 marketing insights with you:1. Classic SEO is no longer efficient because of AI Overviews on Google 2. Users referred by AIconvert at 4.4x the rate of traditional organic visitors 3. Paid ads on Google, Instagram, LinkedIn, etc are getting more and more expensive and CR is declining.This is a new reality we (marketers) live in – and we have to adapt if we want to stay relevant.That's why I created GTM in Public – to share real marketing and business growth experiments in public.If you're a marketer, a solo founder, a content creator – or a serial entrepreneur – you will enjoy what I share.Welcome. → GTM in Public
Machine Learning & Artificial Intelligence | Data Science Free Courses
29 Aug, 12:35edited
13. Use Cross-ValidationDon't rely on a single train-test split when evaluating models, especially when the dataset is limited. Cross-validation gives you a more robust estimate of model performance.📌 14. Keep Your Experiments ReproducibleRecord: Dataset version, Features used, Model, Hyperparameters, Evaluation metrics, Random seeds, Experiment resultsYou should be able to answer: "How did we get this result?"📌 15. Compare Models FairlyWhen comparing models, use the same: Dataset splits, Evaluation metrics, Validation strategy, Target definitionOtherwise, your comparison may not be meaningful.📌 16. Learn to Interpret Your ModelsDon't stop at: "The model predicted 0.87."
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