Course details

Building ML Training Sets: Introduction

Building ML Training Sets: Introduction


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
  There are numerous options available to scale and encode features and labels in data sets to get the best out of machine learning (ML) algorithms. In this 10-video course, explore techniques such as standardizing, nomalizing, and one-hot encoding. Learners begin by learning how to use Pandas library to load a data set in the form of a CSV file and perform exploratory analysis on its features. Then use scikit-learn's Binarizer to transform the continuous data in a series to binary values; apply the MiniMaxScaler on a data set to get two similar columns to have the same range of values; and standardize multiple columns in data sets with scikit-learn's StandardScaler. Examine differences between the Normalizer and other scaling techniques, and learn how to represent values in a column as a proportion of the maximum absolute value by using the MaxAbScaler. Finally, discover how to use Pandas library to one-hot encode one or more features of your data set and distinguish between this technique and label encoding. The concluding exercise involves building ML training sets.


Expected Duration (hours)
1.2

Lesson Objectives

Building ML Training Sets: Introduction

  • Course Overview
  • use the Pandas library to load a dataset in the form of a CSV file and perform some exploratory analysis on its features
  • transform the continuous data in a series to binary values by using scikit-learn's Binarizer
  • apply the MinMaxScaler on a dataset to get two similar columns to have the same range of values
  • standardize multiple columns in your dataset using scikit-learn's StandardScaler
  • distinguish between the Normalizer and other scaling techniques and apply this scaler on the continuous features of a dataset
  • represent the values in a column as a proportion of the maximum absolute value by using the MaxAbsScaler
  • apply label encoding on the features and target in your dataset and recognize its limitations when applied on input features
  • use the Pandas library to one-hot encode one or more features of your dataset and distinguish between this technique and label encoding
  • transform a continuous series into a categorical (binary) one, distinguish between Normalization and other scaling techniques, score each product as a proportion of the top product’s sales, and encode the ”VehicleType” field which contains values [“Hatchback”, “Sedan”, “SUV”]
  • Course Number:
    it_mlbmltdj_01_enus

    Expertise Level
    Beginner