@dunglamabuon1110: tuấn anh dragon #ppt #tuananh #116ppt #gianghomang #gianghomom

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Thursday 08 October 2026 03:23:24 GMT

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ta. :

2026-10-08 03:46:58

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vy đại nhân :

Anh tôi đấy

2026-10-08 04:39:36

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ngầu đét

2026-10-08 03:51:09

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tit :

@chutt tuất anh

2026-10-08 03:29:59

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@huynh nhu bá khí

2026-10-08 04:18:31

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Don’t try to learn everything at once. Follow this roadmap step by step 👇 1️⃣ PYTHON 🐍 Start with the language you’ll use throughout your journey. Learn: • Variables & data types • Conditions & loops • Functions • Lists, tuples, sets & dictionaries • OOP • Exception handling • File handling • Modules & packages • Virtual environments 2️⃣ SQL 🗄️ Data Scientists work with databases constantly. Learn: • SELECT & WHERE • GROUP BY & HAVING • JOINs • Subqueries • CTEs • CASE statements • Window functions • Ranking • Date & time analysis 3️⃣ MATHEMATICS 🧮 You don’t need every branch of mathematics. Focus on: • Algebra • Functions • Exponents & logarithms • Vectors • Matrices • Probability • Statistics • Calculus basics • Optimization 4️⃣ DATA ANALYSIS 📊 Learn how to turn raw data into useful information. Master: 🐼 Pandas 🔢 NumPy 📈 Matplotlib 🎨 Seaborn Practice: Cleaning → EDA → Visualization → Insights 5️⃣ STATISTICS 📐 Understand the mathematics behind your data. Learn: • Mean, median & mode • Variance & standard deviation • Probability distributions • Sampling • Confidence intervals • Hypothesis testing • Correlation • Regression • A/B testing 6️⃣ MACHINE LEARNING 🤖 Now start building predictive models. Learn: Supervised Learning • Linear Regression • Logistic Regression • Decision Trees • Random Forest • Gradient Boosting • SVM • KNN Unsupervised Learning • K-Means • Hierarchical Clustering • PCA 7️⃣ MODEL EVALUATION 🎯 Training a model isn’t enough. Understand: • Train / validation / test sets • Cross-validation • Accuracy • Precision • Recall • F1-score • ROC-AUC • MAE / MSE / RMSE • Overfitting & underfitting • Bias-variance tradeoff 8️⃣ FEATURE ENGINEERING ⚙️ Learn how to improve the information given to your model. Practice: • Encoding • Scaling • Feature selection • Transformation • Missing-value handling • Outlier treatment • Feature creation 9️⃣ DEEP LEARNING 🧠 Move beyond traditional ML. Learn: • Neural Networks • Backpropagation • Activation functions • Optimizers • CNNs • RNNs / LSTMs • Transfer Learning • Transformers Tools: PyTorch / TensorFlow 🔟 GENERATIVE AI 🤖✨ Modern Data Science increasingly overlaps with AI engineering. Learn: • LLMs • Prompt Engineering • Embeddings • Vector Databases • RAG • Fine-tuning • AI Agents • Tool Calling • LLM Evaluation • Hallucination & reliability 1️⃣1️⃣ ML ENGINEERING 🚀 Turn your model into a real application. Learn: • Git & GitHub • APIs • FastAPI • Docker • Cloud basics • Model serving • CI/CD • Monitoring • Model drift • MLOps 1️⃣2️⃣ BUILD REAL PROJECTS 🏗️ Don’t wait until you “finish learning.” Build while learning. Beginner: 📊 EDA + Dashboard Intermediate: 🤖 End-to-End ML Project Advanced: 🧠 Deep Learning Project Modern AI: 🔎 RAG Application Production: 🚀 Deployed ML/AI System 🏆 FINAL ROADMAP Python ⬇️ SQL ⬇️ Math + Statistics ⬇️ Pandas + NumPy ⬇️ EDA + Visualization ⬇️ Machine Learning ⬇️ Model Evaluation ⬇️ Feature Engineering ⬇️ Deep Learning ⬇️ Generative AI ⬇️ ML Engineering + MLOps ⬇️ Real Projects ⬇️ Portfolio + Job Applications 💼 🔥 THE MOST IMPORTANT PART Don’t learn: Python → SQL → ML → DL → AI → then start building. Instead: Learn → Build → Break → Debug → Improve → Explain → Repeat. Your portfolio should prove that you can take: DATA → INSIGHT → MODEL → APPLICATION → REAL-WORLD VALUE 📌 Save this roadmap and use it as your Data Science checklist. #DataScience #DataScientist #MachineLearning #creatorsearchinsights #datascientist

Don’t try to learn everything at once. Follow this roadmap step by step 👇 1️⃣ PYTHON 🐍 Start with the language you’ll use throughout your journey. Learn: • Variables & data types • Conditions & loops • Functions • Lists, tuples, sets & dictionaries • OOP • Exception handling • File handling • Modules & packages • Virtual environments 2️⃣ SQL 🗄️ Data Scientists work with databases constantly. Learn: • SELECT & WHERE • GROUP BY & HAVING • JOINs • Subqueries • CTEs • CASE statements • Window functions • Ranking • Date & time analysis 3️⃣ MATHEMATICS 🧮 You don’t need every branch of mathematics. Focus on: • Algebra • Functions • Exponents & logarithms • Vectors • Matrices • Probability • Statistics • Calculus basics • Optimization 4️⃣ DATA ANALYSIS 📊 Learn how to turn raw data into useful information. Master: 🐼 Pandas 🔢 NumPy 📈 Matplotlib 🎨 Seaborn Practice: Cleaning → EDA → Visualization → Insights 5️⃣ STATISTICS 📐 Understand the mathematics behind your data. Learn: • Mean, median & mode • Variance & standard deviation • Probability distributions • Sampling • Confidence intervals • Hypothesis testing • Correlation • Regression • A/B testing 6️⃣ MACHINE LEARNING 🤖 Now start building predictive models. Learn: Supervised Learning • Linear Regression • Logistic Regression • Decision Trees • Random Forest • Gradient Boosting • SVM • KNN Unsupervised Learning • K-Means • Hierarchical Clustering • PCA 7️⃣ MODEL EVALUATION 🎯 Training a model isn’t enough. Understand: • Train / validation / test sets • Cross-validation • Accuracy • Precision • Recall • F1-score • ROC-AUC • MAE / MSE / RMSE • Overfitting & underfitting • Bias-variance tradeoff 8️⃣ FEATURE ENGINEERING ⚙️ Learn how to improve the information given to your model. Practice: • Encoding • Scaling • Feature selection • Transformation • Missing-value handling • Outlier treatment • Feature creation 9️⃣ DEEP LEARNING 🧠 Move beyond traditional ML. Learn: • Neural Networks • Backpropagation • Activation functions • Optimizers • CNNs • RNNs / LSTMs • Transfer Learning • Transformers Tools: PyTorch / TensorFlow 🔟 GENERATIVE AI 🤖✨ Modern Data Science increasingly overlaps with AI engineering. Learn: • LLMs • Prompt Engineering • Embeddings • Vector Databases • RAG • Fine-tuning • AI Agents • Tool Calling • LLM Evaluation • Hallucination & reliability 1️⃣1️⃣ ML ENGINEERING 🚀 Turn your model into a real application. Learn: • Git & GitHub • APIs • FastAPI • Docker • Cloud basics • Model serving • CI/CD • Monitoring • Model drift • MLOps 1️⃣2️⃣ BUILD REAL PROJECTS 🏗️ Don’t wait until you “finish learning.” Build while learning. Beginner: 📊 EDA + Dashboard Intermediate: 🤖 End-to-End ML Project Advanced: 🧠 Deep Learning Project Modern AI: 🔎 RAG Application Production: 🚀 Deployed ML/AI System 🏆 FINAL ROADMAP Python ⬇️ SQL ⬇️ Math + Statistics ⬇️ Pandas + NumPy ⬇️ EDA + Visualization ⬇️ Machine Learning ⬇️ Model Evaluation ⬇️ Feature Engineering ⬇️ Deep Learning ⬇️ Generative AI ⬇️ ML Engineering + MLOps ⬇️ Real Projects ⬇️ Portfolio + Job Applications 💼 🔥 THE MOST IMPORTANT PART Don’t learn: Python → SQL → ML → DL → AI → then start building. Instead: Learn → Build → Break → Debug → Improve → Explain → Repeat. Your portfolio should prove that you can take: DATA → INSIGHT → MODEL → APPLICATION → REAL-WORLD VALUE 📌 Save this roadmap and use it as your Data Science checklist. #DataScience #DataScientist #MachineLearning #creatorsearchinsights #datascientist

Không chỉ bảo đảm tốt phương tiện, phong trào “Xây dựng đơn vị vận tải chính quy, an toàn, hiệu quả” đã tạo nên sức mạnh cơ động bền bỉ, giúp lực lượng vận tải hoàn thành thắng lợi mọi nhiệm vụ huấn luyện, sẵn sàng chiến đấu cũng như kịp thời có mặt giúp nhân dân trong mọi tình huống. #quandoinhandanvietnam #camerachiensi #qpvn

Không chỉ bảo đảm tốt phương tiện, phong trào “Xây dựng đơn vị vận tải chính quy, an toàn, hiệu quả” đã tạo nên sức mạnh cơ động bền bỉ, giúp lực lượng vận tải hoàn thành thắng lợi mọi nhiệm vụ huấn luyện, sẵn sàng chiến đấu cũng như kịp thời có mặt giúp nhân dân trong mọi tình huống. #quandoinhandanvietnam #camerachiensi #qpvn

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