Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
Feature Engineering
In order to predict the outcome variable (y variable) you need to turn all the object type columns into numeric in order to predict the Sale Price. So first, I converted all the ordinal columns into numeric by assigning them by numbers, and for all the other features I created dummy variables.
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Modelling
After creating all the features to predict the sale prices, using sklearn, we train-test-split the training data. In Regression, there are a lot of models you can choose from to get the best performing model. In this project, I ran Linear Regression, Ridge, and Lasso models.
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Types Of Data
- Nominal: used for labelling variables(m- male and f- female)
- Ordinal: used for measuring non-numeric with an order of the values(1-unhappy, 2-ok, 3- happy)
- Data Cleaning: In this data set, there are 2051 rows with 80 colum...
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Machine Learning Explained
Machine Learning is the process of letting your machine use the data to learn the relationship between predictor variables and the target variable. It is one of the first steps toward becoming a data scientist.
There are two kinds of Machine Learning: supervised, and unsupervised learning....
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A machine learning example
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Types Of Data
- Nominal: used for labelling variables(m- male and f- female)
- Ordinal: used for measuring non-numeric with an order of the values(1-unhappy, 2-ok, 3- happy)
- Data Cleaning: In this data set, there are 2051 rows with 80 colum...
Feature extraction and suitable machine learning model
When dealing with large datasets with many columns and variables, feature extracting is used to divide and reduce existing data into a manageable group.
But for image processing, machines can't extract features such as edges, shapes, or even size in this way
Transfer learning and fine tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
- Take layers from...
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