Dive into a comprehensive tutorial series on Python machine learning, progressing from foundational concepts like linear regression and k-nearest neighbors to advanced topics such as neural networks and convolutional neural networks. Learn to implement various algorithms, save models, plot data, and work with irregular datasets. Explore the inner workings of Support Vector Machines and K-Means Clustering, and gain hands-on experience using scikit-learn datasets throughout the 2.5-hour course.
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
