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 Clustering49mMachine 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 18mMachine 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.