Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# Definition: Split validation

A crucial part of machine learning is partitioning the data into two separate sets using a technique called split validation.
 
- The first set is called the training data and is used to build the prediction model. 

- The second set is called the test data and is kept in reserve to assess the model’s accuracy developed from the training data.

- The training and test data is typically split 70/30 or 80/20, with the training data representing the larger portion. Once the model has been optimized and validated against the test data for accuracy, it’s ready to generate predictions using new input data.

Although the model is used on both the training and test sets, it’s from the training data alone that the model is built.

The test data is used as input to form predictions and assess the model’s accuracy, but it is never decoded and should not be used to create the model. Since the test data cannot be used to build and optimize the model, data scientists sometimes use a third independent dataset called the validation set. 

After building an initial model with the training set, the validation set can be fed to the prediction model and used as feedback to optimize the model’s hyperparameters. The test set is then used to assess the prediction error of the final model.

To maximize data utility, it is possible to reuse the validation and test data as training data. This would involve bundling the used data with the original training data to optimize the model just before it’s put into use. 

> However, once the original validation or test set has been used for training, it can no longer be used as a validation or test set.

# Train and test set

To perform split validation in Python, you can use `train_test_split` from `Scikit-learn,` which requires an initial import from the `sklearn.model_selection` library.
```python
from sklearn.model_selection import train_test_split
```
Before using this code library, you first need to set our x and y values.
```Python
import pandas as pd
df = pd.read_csv('~/Downloads/advertising.csv')
X = df[['Daily Time Spent on Site', 'Age', 'Area Income', 'Daily Internet Usage', 'Ad Topic Line', 'Country']]
y = df['Clicked on Ad']
```
> You are now ready to create our training and test data using the following parameters: `train_size` (optional), `test_size, random_state` (optional), and `shuffle` (optional).



An introduction to train, validate and test data split using sklearn.

Quick Overview of Split Validation

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

Quick Overview of Split Validation

Definition: Split validation