Advance Your Skills as a Machine Learning Specialist

Advance your machine learning expertise with this comprehensive learning path. Explore core and advanced topics, including predictive analytics, decision trees, causal inference, deep learning optimization, and the differences between machine learning and statistical methods. Through real-world examples and tools like KNIME Analytics Platform, gain practical skills to boost model transparency and effectiveness. Start now to elevate your skills.

Explore how to design machine learning algorithms.
Learn how recommendation systems work and how to build them.
Practice designing machine solutions for applications.
Implement decision trees using the KNIME Analytics Platform.

Syllabus

Courses under this program:
Course 1: Machine Learning with Python: Decision Trees
-Learn how to build decision trees in Python to measure impurity within a partition and improve outcomes on machine learning projects.

Course 2: Machine Learning with Python: k-Means Clustering
-Learn the basics of k-means clustering, one of the most popular unsupervised machine learning approaches.

Course 3: Machine Learning with Python: Association Rules
-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.

Course 4: Machine Learning with Python: Logistic Regression
-Get an introduction to logistic regression by exploring how to build supervised machine learning models with Python.

Course 5: Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions
-Learn best practices for how to produce explainable AI and interpretable machine learning solutions.

Course 6: Machine Learning and AI Foundations: Decision Trees with KNIME
-Expand your data science skills and establish a strong foundation in codeless machine learning.

Course 7: Machine Learning and AI Foundations: Causal Inference and Modeling
-Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.

Course 8: Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference
-Gain insights to help improve your machine learning models and statistical analyses.

Course 9: Deep Learning: Model Optimization and Tuning
-Learn about various optimization and tuning options available for deep learning models and use them to improve models.

Taught by

Keith McCormick, Dan Sullivan, Lillian Pierson, P.E., Keith McCormick, Keith McCormick, Keith McCormick, Adam Geitgey, Keith McCormick and Adam Geitgey