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

## 1) Import libraries
While decision trees can solve regression or classification problems, we will use the classification version of the algorithm as we will be predicting a discrete variable. Using the `DecisionTreeClassifier` algorithm from `Scikit-learn`, we will attempt to predict the dependent variable `Clicked on Ad` (0 or 1). The performance of the model will be evaluated using a classification report and a confusion matrix.




# 1) Import libraries
While decision trees can solve regression or classification problems, we will use the classification version of the algorithm as we will be predicting a discrete variable. Using the `DecisionTreeClassifier` algorithm from `Scikit-learn`, we will attempt to predict the dependent variable `Clicked on Ad` (0 or 1). The performance of the model will be evaluated using a classification report and a confusion matrix.




We will start to get familiarized with the implementation steps (1-4) of decision trees.

DT Implementation Steps: 1 to 4

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

DT Implementation Steps: 1 to 4

1) Import libraries