Scikit-learn Essentials: Mastering the Scikit-learn Machine Learning Library for Python Video
- Understand the use cases for scikit-learn and learn about the core of scikit-learn which is the estimator API.
- Install scikit-learn.
- Load data using scikit-learn.
- Learn about Sylearn, which accepts data as either a numpy array or a pandas data frame.
- Apply transformations to the data before feeding data to the algorithm.
- Learn how to use sklearn.
- Practice the six steps to follow in pre-processing data: mean removal and variance scaling, non-linear transformation, normalization, encoding categorical features, discretization, and the imputation of missing values.
- Perform Train-Test-Split in scikit-learn.
- Apply linear regression in scikit-learn. Linear regression is a statistical model that is used for finding linear relationships between a target and one or more predictors.
- Learn how to set up dependent variables and independent variables and the two types of linear regression (simple linear regression and multiple linear regression).
- Apply the Naïve Bayes classifier in scikit-learn.
- Understand the concept of feature independence and Bayes Theorem of probability.
- Apply the Support Vector Machines supervised machine learning algorithm in scikit-learn.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date