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

Which solution will best shrink and simplify the original dataset?

Which solution will best shrink and simplify the original dataset?

StandardScaler is often used in conjunction with PCA and which other algorithm to rescale and standardize features?

StandardScaler is often used in conjunction with PCA and which other algorithm to rescale and standardize features?

For k-means clustering, which technique might we use to find the appropriate number of clusters?

For k-means clustering, which technique might we use to find the appropriate number of clusters?

Using k-means clustering, the total number of centroids should be lower than the total number of variables in the original dataset. True or False?

Using k-means clustering, the total number of centroids should be lower than the total number of variables in the original dataset. True or False?

In Python, you can use ___ to code a scree plot.

In Python, you can use ___ to code a scree plot.

Principal component analysis can be applied to both discrete and continuous variables. True or False?

Principal component analysis can be applied to both discrete and continuous variables. True or False?

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

Quiz