Catalog Search Results
Author
Publisher
linkedin.com
Pub. Date
2020.
Language
English
Description
Learn how to hire and manage data science professionals and transform your business with effectively deployed advanced analytics.
Organizations in nearly every industry are seeking and hiring data scientists, but many of these professionals don't remain at their posts for long. Even though data analytics skills are highly valued, individuals with this skill set can't make an impact unless middle and senior management know how to leverage analytics...
Author
Publisher
linkedin.com
Pub. Date
2017.
Language
English
Description
Establish a strong foundation in ML by exploring the IBM SPSS Modeler and learning about CHAID and C&RT. This course is designed to help expand your data science skills.
Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering...
Author
Publisher
linkedin.com
Pub. Date
2023.
Language
English
Description
Learn about AutoML, the opportunities and challenges that arise in attempting to automate machine learning, and how this automation affects your organization.
An increasing number of open-source and commercial vendors are attempting to automate machine learning (ML), and analytics leaders need to know how this impacts data science and machine learning in their organizations. In this course, machine learning specialist, trainer, and author Keith McCormick...
Author
Publisher
linkedin.com
Pub. Date
2021.
Language
English
Description
Turn predictive analytics into a profit center at your organization. Learn how to measure the return on investment (ROI) and prove the long-term value of your projects.
Nothing is more important to the future of predictive analytics teams than proving their projects have long-term value. Measuring the return on investment (ROI) often can help turn analytics into a visible profit center for your organization. Estimating ROI early-before a project...
Author
Publisher
linkedin.com
Pub. Date
2018.
Language
English
Description
Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
Unsupervised learning is a type of machine learning where algorithms parse unlabeled data. The focus is not on sorting data into known categories but uncovering hidden patterns. Unsupervised learning plays a big role in modern marketing segmentation, fraud detection, and market basket analysis. This course shows how to use leading machine-learning...
Author
Publisher
linkedin.com
Pub. Date
2022.
Language
English
Description
Learn best practices for how to produce explainable AI and interpretable machine learning solutions.
Data scientists and machine learning professionals have to stay apace with the latest techniques and approaches in the field. In this course, instructor Keith McCormick shows you how to produce explainable AI (XAI) and interpretable machine learning (IML) solutions. Learn why the need for XAI has been rapidly increasing in recent years. Explore available...
Author
Publisher
linkedin.com
Pub. Date
2020.
Language
English
Description
Explore the data understanding phase of the CRISP-DM methodology for predictive modeling. Find out how to collect, describe, explore, and verify data.
CRISP-DM, the cross-industry standard process for data mining, is composed of six phases. Most new data scientists rush to modeling because it's the phase in which they have the most training. But whether the project succeeds or fails is actually determined far earlier. This course introduces a systematic...
Author
Publisher
linkedin.com
Pub. Date
2018.
Language
English
Description
Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.
Having a solid understanding of linear regression-a method of modeling the relationship between one dependent variable and one to several other variables-can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock...
Author
Publisher
linkedin.com
Pub. Date
2022.
Language
English
Description
Expand your data science skills and establish a strong foundation in codeless machine learning.
Suggested prerequisites General familiarity with supervised machine learning Understanding of terms such as target variable, input variable, algorithm, and train/test partition Decision trees are transparent, available in every platform, and foundational to more advanced techniques like Random Forests and XGBoost. And if you're a data scientist looking...
Author
Publisher
linkedin.com
Pub. Date
2022.
Language
English
Description
Gain insights to help improve your machine learning models and statistical analyses.
In the world of data science, machine learning and statistics are often lumped together, but they serve different purposes, and being versed in one doesn't mean expertise in the other. In fact, applying a statistical approach to a machine learning problem, or vice versa, can lead to confusion more than elucidation. In this course, Keith McCormick covers how stats...
Author
Publisher
linkedin.com
Pub. Date
2020.
Language
English
Description
Learn the nontechnical skills that effective data scientists must nurture to convert their first job into a successful, lifelong career.
Most data science training focuses only on key technologies. But real-world data science jobs require more than just technical acumen. When new data scientists change their focus from the classroom to the boardroom, they must be able to empathize, persuade, and lead others if they want to successfully run projects...
Author
Publisher
linkedin.com
Pub. Date
2023.
Language
English
Description
Explore the core concepts and key skills of human-in-the-loop machine learning, including how to successfully implement and manage a data annotation project.
Human-in-the-loop machine learning is all about continuous learning. And as a process, it's becoming an increasingly common and critical component of emerging technologies. From healthcare analytics and computer vision to autonomous vehicles and natural language processing, human-in-the-loop...
Author
Publisher
linkedin.com
Pub. Date
2019.
Language
English
Description
Learn how to use ensembles and metamodeling to create more accurate predictive models.
Ensembles involve groups of models working together to make more accurate predictions. When creating complete deployed solutions, data scientists may also leverage passing data from one model to another or using models in combination-also known as metamodeling. These techniques are dominant among winners of modeling competitions like Kaggle as well as leading data...
Author
Publisher
linkedin.com
Pub. Date
2022.
Language
English
Description
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.
This course with instructor Keith McCormick provides an introduction to some advanced techniques in causal inference and causal modeling. It builds upon a foundation in Keith's course, Machine Learning and AI Foundations: Prediction, Causality, and Statistical Inference. Keith focuses the course on three major topics: The...
Author
Publisher
linkedin.com
Pub. Date
2019.
Language
English
Description
Learn KNIME, a popular open-source platform for predictive analytics and machine learning. Discover how to use KNIME for merging and aggregation, modeling, data scoring, and more.
KNIME is an open-source workbench-style tool for predictive analytics and machine learning. It is highly compatible with numerous data science technologies, including R, Python, Scala, and Spark. With KNIME, you can produce solutions that are virtually self-documenting...
Author
Publisher
linkedin.com
Pub. Date
2018.
Language
English
Description
Scalability is one of the biggest challenges in data science. Learn how to evaluate data, choose the right algorithms, and perform predictive modeling at scale.
Building world-class predictive analytics solutions requires recognizing that the challenges of scale and sample size fluctuate greatly at different stages of a project. How do you know how much data to use? What is too little, what is too much? How does your infrastructure need to scale...
Author
Publisher
linkedin.com
Pub. Date
2022.
Language
English
Description
Learn to go beyond the basic decision tree algorithms in KNIME by accessing WEKA, R, and Python-based decision tree and rule induction algorithms from within the KNIME platform.
Every year, it seems, there is a new hot trend in data science. One of the hottest predictive analytics algorithms this year is gradient-boosted trees. One cannot hope to understand why it is popular and successful if one doesn't understand the basics of decision trees. Specific...
Author
Publisher
linkedin.com
Pub. Date
2018.
Language
English
Description
Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.
One type of problem absolutely dominates machine learning and artificial intelligence: classification. Binary classification, the predominant method, sorts data into one of two categories: purchase or not, fraud or not, ill or not, etc. Machine learning and AI-based solutions need accurate, well-chosen...
Author
Publisher
linkedin.com
Pub. Date
2021.
Language
English
Description
Get useful, real-world insights into using predictive analysis and data mining to solve problems.
Are you a data science practitioner, looking to develop or enhance your skills in predictive analysis and data mining? This course provides several "big picture" insights, via instructor Keith McCormick, a veteran practitioner who has completed dozens of real-world projects. Keith begins by introducing you to key definitions and processes that you will...
Author
Publisher
linkedin.com
Pub. Date
2020.
Language
English
Description
Diversify your career prospects by integrating a side hustle with your existing responsibilities. Learn how to leverage your data science and analytics skills to work for yourself.
The most common way to start a career in data science is to master the technical aspects of the job and then apply to work at a large company. But many of the most successful data scientists are also entrepreneurs that work for themselves. In this course, discover how...
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