Introduction to scikit-learn. Scikit-Learn is a Machine Learning Library for Python. These slides will guide you through some basics.

1. 1617 – Akshay Gaonkar

3. scikit-learn
Machine Learning in Python
Simple and efficient tools for data mining and data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable - BSD license

4. Scikit Provides
Classification
Identifying to which category an object belongs to.
Applications: Spam detection, Image recognition.
Algorithms: SVM, nearest neighbors, random forest

5. Scikit Provides
Regression
Predicting a continuous-valued attribute associated with an object.
Applications: Drug response, Stock prices.
Algorithms: SVR, ridge regression, Lasso

6. Scikit Provides
Clustering
Automatic grouping of similar objects into sets.
Applications: Customer segmentation, Grouping experiment outcomes.
Algorithms: k-Means, spectral clustering, mean-shift

7. Scikit Provides
Dimensionality reduction
Reducing the number of random variables to consider.
Applications: Visualization, Increased efficiency
Algorithms: PCA, feature selection, non-negative matrix
factorization

8. Scikit Provides
Model selection
Comparing, validating and choosing parameters and models.
Goal: Improved accuracy via parameter tuning.
Modules: grid search, cross validation, metrics.

9. Scikit Provides
Preprocessing
Feature extraction and normalization.
Goal: Transforming input data such as text for use with machine
learning algorithms.
Modules: preprocessing, feature extraction.

10. Most Importantly
Good Documentaion
Good Community Support

11. Installation
Scikit-learn requires:
Python (>= 2.7 or >= 3.3) with pip
NumPy (>= 1.8.2)
SciPy (>= 0.13.3)
Scikit-learn install:
python -m pip install -U pip
pip install scikit-learn
pip install numpy
pip install scipy
pip install matplotlib

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14. Who is using scikit-learn?
and many more ..

15. Thank You