Scikit-learn Essentials: Mastering the Scikit-learn Machine Learning Library for Python Video

Scikit-learn Essentials: Mastering the Scikit-learn Machine Learning Library for Python Video

Objectives:

1. Understand the use cases for scikit-learn and learn about the core of scikit-learn which is the estimator API.
2. Install scikit-learn.
3. Load data using scikit-learn.
4. Learn about Sylearn, which accepts data as either a numpy array or a pandas data frame.
5. Apply transformations to the data before feeding data to the algorithm.
6. Learn how to use sklearn.
7. 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.
8. Perform Train-Test-Split in scikit-learn.
9. 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.
10. Learn how to set up dependent variables and independent variables and the two types of linear regression (simple linear regression and multiple linear regression).
11. Apply the Naïve Bayes classifier in scikit-learn.
12. Understand the concept of feature independence and Bayes Theorem of probability.
13. 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