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@nhungnhayday98: Màu hồng lưới tăng 1 sz #giaythethao#xuhuong

Nhung nháy đây 💋
Nhung nháy đây 💋
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Thursday 20 August 2026 05:23:47 GMT
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If you want to grow into AI in 2026–27, don’t start with Machine Learning immediately. Build a real foundation. This is the clean roadmap I wish someone gave me earlier. Save this and follow step by step: 1. Start with Data Analysis Excel → SQL → Python basics → Pandas + NumPy → Data Cleaning → EDA → Visualization (Matplotlib, Seaborn). 2. Build Your Python Core Functions, OOP, File handling, APIs, Error handling. Don’t skip this part. 3. Machine Learning Essentials Supervised learning → Regression & Classification → Model evaluation → Feature engineering → Regularization → Train/test splits → Cross-validation. 4. Intermediate ML (2026–27) Tree models → Random Forest → XGBoost → Pipelines → Hyperparameter tuning. 5. Advanced Track Neural networks → CNNs → RNNs → Transformers (only after strong basics). 6. Deployment Layer Streamlit → FastAPI → GitHub → Docker (optional but powerful). 7. Must-do Projects Student performance prediction, fraud detection, customer segmentation, recommendation system, sales forecasting. 💬 Question for comments (to boost engagement): Which level are you at right now — Data Analysis, ML Basics, or Advanced ML? Drop it below so I can guide you. #creatorsearchinsights #DataAnalysis #MachineLearningRoadmap #AI2026 #LearnMachineLearning
If you want to grow into AI in 2026–27, don’t start with Machine Learning immediately. Build a real foundation. This is the clean roadmap I wish someone gave me earlier. Save this and follow step by step: 1. Start with Data Analysis Excel → SQL → Python basics → Pandas + NumPy → Data Cleaning → EDA → Visualization (Matplotlib, Seaborn). 2. Build Your Python Core Functions, OOP, File handling, APIs, Error handling. Don’t skip this part. 3. Machine Learning Essentials Supervised learning → Regression & Classification → Model evaluation → Feature engineering → Regularization → Train/test splits → Cross-validation. 4. Intermediate ML (2026–27) Tree models → Random Forest → XGBoost → Pipelines → Hyperparameter tuning. 5. Advanced Track Neural networks → CNNs → RNNs → Transformers (only after strong basics). 6. Deployment Layer Streamlit → FastAPI → GitHub → Docker (optional but powerful). 7. Must-do Projects Student performance prediction, fraud detection, customer segmentation, recommendation system, sales forecasting. 💬 Question for comments (to boost engagement): Which level are you at right now — Data Analysis, ML Basics, or Advanced ML? Drop it below so I can guide you. #creatorsearchinsights #DataAnalysis #MachineLearningRoadmap #AI2026 #LearnMachineLearning
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