This course teaches basic and advanced machine learning concepts such as linear regression, k-nearest neighbors, neural networks, and convolutional neural networks. Students will learn how to implement these algorithms in Python, use Sklearn datasets, save models, and plot data. The teaching method includes tutorials with practical implementations. The course is intended for individuals interested in learning Python for machine learning applications.
Overview
Syllabus
Python Machine Learning Tutorial #1 - Introduction.
Python Machine Learning Tutorial #2 - Linear Regression p.1.
Python Machine Learning Tutorial #3 - Linear Regression p.2.
Python Machine Learning Tutorial #4 - Saving Models & Plotting Data.
Python Machine Learning Tutorial #5 - KNN p.1 - Irregular Data.
Python Machine Learning Tutorial #6 - KNN p.2 - How does K Nearest Neighbors Work?.
Python Machine Learning Tutorial #7 - KNN p.3 - Implementation.
Python Machine Learning Tutorial #8 - Using Sklearn Datasets.
Python Machine Learning Tutorial #9 - SVM P.2 - How Support Vector Machines Work.
Python Machine Learning Tutorial #10 - SVM P.3 - Implementing a SVM.
Python Machine Learning Tutorial #11 - How K Means Clustering Works.
Python Machine Learning Tutorial #12 - Implementing K-Means Clustering.
Taught by
Tech With Tim
