**Table of Contents**

- Machine Learning
- What is scikit learn or sklearn
- Dependencies installation
- Installing and environment Setup
- Exploring Sklearn
- Video Tutorial

**1 Machine Learning**

- Machine learning is a subdomain of Artificial Intelligence that teaches machines to perform specific tasks by using algorithms and statistical models.
- Machine learning algorithms has a wide variety of applications in every major domain like language processing ,computer vision , time series analysis and many more.
- Python is most used language for machine learning tasks and Scikit learn is one of the most used libraries for ml tasks.

**2 What is scikit learn or sklearn**

- scikit-learn is a Python library and it’s open-source allowing it to have a huge development over a period of time and it’s easy implementation makes this one of the most famous libraries in python implementation.
- Sklearn makes our path easy in machine learning life cycles.
- It is used for Data Preprocessing (Min-Max scalar, Standardization, Normalization)
- Splitting data (train and test)
- Feature Selection
- Training the models (Linear models, Tree Models, Ensemble models, Neural Networks)
- Evaluating with different metrics.(Accuracy,RMSE,F1,recall,precision)………….many more

**3 Dependencies installation**

scikit-learn is mainly built on main three libraries of numpy, SCIPY,matplotlib

*NumPy and SciPy*

These are very fundamental libraries of machine-learning implementation in python because it allows the users to implement the multi-dimensional array and vectorization which increases the speed of the models drastically

*Matplotlib*

Itis a plotting or visualization library allowing the users to plot a different kind of bars and charts and easily through visualization to get inside out of data.

**installation of scikit-learn **

*Scikit-learn requirements*

- Python (> =2.7 or Python > =3.4)
- NumPy(≥1.8.2)
- Scipy (≥0.13.3)

*Command to install Scikit-learn:*

**5 Exploring Sklearn**

- Scikit-learn is organized in a structure of modules and classes. Importing those modules and classes are very straight forward.
- Let see how we can call preprocessing Subclass from the Sklearn library.

Similarly, we can easily evaluate models with different metrics using sklearn.

Sklearn gives high flexibility such that we can handle regression, classification, and clustering models elegantly.