32. Normalization in practice

🎓 Data Science – Full Course | Machine Learning & AI

A comprehensive Data Science course covering everything from fundamentals to advanced Machine Learning and NLP techniques. Ideal for students and developers looking to build practical data science skills.

📘 Topics covered:
• Python setup: Anaconda, Jupyter Notebook, ML libraries
• Data Science methodology: CRISP-DM model
• Data quality, cleaning & munging
• Feature engineering: normalization, encoding, feature importance
• Machine Learning algorithms: Decision Tree, Linear Regression, Logistic Regression, Gradient Descent
• Real-world hotel booking data analysis
• Natural Language Processing (NLP): corpus, TF-IDF, sentiment analysis, topic modeling, text generation
• Neural Networks & Multi-Layer Perceptron (MLP)

📚 Suitable for students, software developers, and anyone starting their journey in Data Science, Machine Learning, and AI.

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Видео 32. Normalization in practice канала KIẾN THỨC 360