Dive into designing and building machine learning algorithms. This learning path shows how machine learning algorithms work and how to design them yourself. There's a lot to learn in this rapidly growing and sought-after field, and these courses give you an extremely solid skill set.
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Explore how to design machine learning algorithms.
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Learn how recommendation systems work and how to build them.
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Practice designing machine solutions for applications.
Courses
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1
Machine Learning with Python: Decision Trees1h 14mMachine Learning with Python: Decision Trees
By: Frederick Nwanganga
Learn how to build decision trees in Python to measure impurity within a partition and improve outcomes on machine learning projects.
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2
Machine Learning with Python: k-Means Clustering50mMachine Learning with Python: k-Means Clustering
By: Frederick Nwanganga
Learn the basics of k-means clustering, one of the most popular unsupervised machine learning approaches.
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3
Machine Learning with Python: Association Rules1h 27mMachine Learning with Python: Association Rules
By: Frederick Nwanganga
Explore the unsupervised machine learning approach known as association rules, as well as a step-by-step guide on how to use the approach for market basket analysis in Python.
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4
Machine Learning with Python: Logistic Regression1h 19mMachine Learning with Python: Logistic Regression
By: Frederick Nwanganga
Get an introduction to logistic regression by exploring how to build supervised machine learning models with Python.
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5
Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions2h 9mMachine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
By: Keith McCormick
Learn best practices for how to produce explainable AI and interpretable machine learning solutions.
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6
Machine Learning and AI Foundations: Decision Trees with KNIME1h 59mMachine Learning and AI Foundations: Decision Trees with KNIME
By: Keith McCormick
Expand your data science skills and establish a strong foundation in codeless machine learning.
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7
Machine Learning and AI Foundations: Causal Inference and Modeling2h 51mMachine Learning and AI Foundations: Causal Inference and Modeling
By: Keith McCormick
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.
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8
Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference2h 2mMachine Learning and AI Foundations: Prediction, Causation, and Statistical Inference
By: Keith McCormick
Gain insights to help improve your machine learning models and statistical analyses.
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9
Deep Learning: Model Optimization and Tuning54mDeep Learning: Model Optimization and Tuning
By: Kumaran Ponnambalam
Learn about various optimization and tuning options available for deep learning models and use them to improve models.
Instructors
Frederick Nwanganga
Associate Teaching Professor of Analytics at the University of Notre Dame - Mendoza College of Business
Keith McCormick
Teaching over a million learners about machine learning, statistics, and Artificial Intelligence (AI) | Data Science Principal at Further
Kumaran Ponnambalam
AI / ML Leader & Author