Scikit Learn Tutorial | Scikit-Learn Workflow | Data Preprocessing In Machine Learning | Intellipaat

Описание: This video on "Data Preprocessing In Machine Learning" will help you understand both the theoretical and implementation details about data preprocessing and how to achieve it with scikit-learn library. The session will begin by bulding programming intuition for scikit-learn algorithmic workflow. Understand the different steps involved in data preprocessing such as handling missing values, value imputation, data scaling, and data encoding. So, let's get started with this second Scikit-Learn tutorial for beginners!

🔵 The topics covered in this tutorial are:
00:00 - Scikit-Learn Workflow
13:49 - Data Cleaning And Preprocessing
24:08 - Value Imputation
36:15 - Feature Engineering
40:27 - Data Encoding
44:30 - Data Scaling


✅What Is Scikit-Learn?
Scikit-learn (previously scikits.learn and also known as sklearn) is a free software machine learning package for Python. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python.

✅Prerequisites to Use Scikit-Learn
1. Python Programming Environment
2. SciPy
3. NumPy
4. Pandas
5. Matplotlib


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