About the course
Utilizing machine learning to apply algorithms to their data has helped companies maximize efficiencies, pursue new markets, and create new products. This trend has prompted many industries to recognize the value of machine learning, creating a high demand for knowledge in this field. This course will cover machine learning foundations and some of the leading open source tools in Python. We will start by learning the various strengths and weaknesses of different machine learning algorithms and then apply them to real-world situations. Additionally, we will touch on use cases where deep learning is appropriate, such as image classification and natural language processing. We will use the Python data science ecosystem to perform machine learning. These tools are open source and popular among data scientists in both academia and industry. The tools we will use include the Jupyter Notebook, Pandas, plotting with Matplotlib and Seaborn, and machine learning with Scikit-Learn. Some of the algorithms we will cover in the course include logistic regression, k-nearest neighbors, decision trees, random forests, principal component analysis, k-means, hierarchical clustering, and neural networks. Students will leave the course with a solid understanding of several machine learning algorithms and the ability to use them when appropriate.
I currently teach Python for Data Visualization for LinkedIn Learning, Data Analytics using Python for UCSD Extension, Machine Learning Fundamentals for UCSD Extension, and Machine Learning with Python for Stanford Continuing Studies. I have previously taught Essential Python for Global Knowledge...
Videos and materials
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