Inhalt: 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 science teams around the world. In this advanced course, you can learn how to add ensembles and metamodeling to your toolset. Instructor Keith McCormick provides a conceptual introduction that can be applied in any program: R, Python, SPSS, or SAS. He introduces the most essential ensemble algorithms and explains the basics of metamodeling. Plus, review two case studies that show how to combine supervised and unsupervised ensembles and how to route subpopulations of data to different models in a metamodeling scenario. Umfang: 01:10:50.00
Inhalt: 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 approach to the data understanding phase for predictive modeling. Instructor Keith McCormick teaches principles, guidelines, and tools, such as KNIME and R, to properly assess a data set for its suitability for machine learning. Discover how to collect data, describe data, explore data by running bivariate visualizations, and verify your data quality, as well as make the transition to the data preparation phase. The course includes case studies and best practices, as well as challenge and solution sets for enhanced knowledge retention. By the end, you should have the skills you need to pay proper attention to this vital phase of all successful data science projects. Umfang: 04:03:20
Inhalt: 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 and ready for use. These reasons and more make KNIME one of the most popular and fastest-growing analytics platforms around. In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. Get up and running quickly-in 15 minutes or less-or stick around for the more in-depth training covering merging and aggregation, modeling, and data scoring. Plus, learn how to increase the power of KNIME with extensions and integrate R and Python. Umfang: 01:41:56.00
Inhalt: 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 prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS. Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting. Umfang: 03:57:20.00
Inhalt: If you're working towards an understanding of machine learning, it's important to know how to work with decision trees. In this course, explore advanced concepts and details of decision tree algorithms. Learn about the QUEST algorithm and how it handles nominal variables, ordinal and continuous variables, and missing data. Explore the C5.0 algorithm and review some of its key features such as global pruning and winnowing. Plus, dive into a few advanced topics that apply to all decision trees, such as boosting and bagging. Umfang: 01:16:34.00
Inhalt: 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 algorithms in order to perform classification correctly. This course explains why predictive analytics projects are ultimately classification problems, and how data scientists can choose the right strategy (or strategies) for their projects. Instructor Keith McCormick draws on techniques from both traditional statistics and modern machine learning, revealing their strengths and weaknesses. Keith explains how to define your classification strategy, making it clear that the right choice is often a combination of approaches. Then, he demonstrates 11 different algorithms for building out your model, from discriminant analysis to logistic regression to artificial neural networks. Finally, learn how to overcome challenges such as dealing with missing data and performing data reduction. Note: These tutorials are focused on the theory and practical application of binary classification algorithms. No software is required to follow along with the course. Umfang: 02:00:59.00
Inhalt: 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 techniques-cluster analysis, anomaly detection, and association rules-to get accurate, meaningful results from big data. Instructor Keith McCormick reviews the most common clustering algorithms: hierarchical, k-means, BIRCH, and self-organizing maps (SOM). He uses the same algorithms for anomaly detection, with additional specialized functions available in IBM SPSS Modeler. He closes the course with a review of association rules and sequence detection, and also provides some resources for learning more. All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software. Umfang: 03:22:11.00
Inhalt: 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 to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills. Umfang: 01:16:18.00
Inhalt: 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 with the volume and demands of the project? This course walks step by step through the strategic and tactical aspects of determining how much data is needed to build an effective predictive modeling solution based on machine learning and what volumes of data are so large that they will create challenges. Instructor Keith McCormick reviews each stage-data selection, data preparation, modeling, scoring, and deployment-with scalability in mind, providing IT professionals, data scientists, and leadership with new insights, perspectives, and collaboration tools. Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only. Umfang: 01:21:26.00
Inhalt: 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 for the long-term benefit of their organization. The challenge is that most of the people overseeing advanced analytics don't have backgrounds in data science themselves. In this course, Keith McCormick shows executives who aren''t fluent in data analytics how to hire data science professionals, manage data science teams, and transform their business with effectively deployed advanced analytics. Keith details how to hire a well-rounded team, including how to identify top-performing data scientists. Plus, he shares how to navigate the different analytics and machine learning software options on the market, fit data science into your organizational structure, and more. Umfang: 01:20:18.00
Inhalt: 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 even begins-can also help fast-track approval. Here Keith McCormick shows how to address ROI both before and after the predictive model is built. Learn how to create your estimate before the project starts by estimating the overall size of the problem, assigning value to possible outcomes, and judging the impact of model performance. Keith then shows a different method for calculating ROI after the model is built, during the evaluation and deployment phases, and provides tips for the ongoing monitoring of the project. He also takes a retrospective look assessed one year after model deployment. These two strategies will give you the data you need to get buy-in for your projects and provide ongoing metrics on their performance. Umfang: 00:51:10
Inhalt: 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 to diversify your career prospects with a side hustle. Instructor Keith McCormick shows how to leverage your data science and analytics skills in a variety of ways-from writing books to delivering talks at conferences. Keith steps through how to jump-start your side hustle in writing by serving as a technical reviewer for technical books. He explains how to use your data science and analytics expertise to become a paid presenter at conferences. Plus, he covers how to build your reputation as a consultant, become a part-time instructor at a university, and more. Along the way, Keith shares tips and strategies for making the most of these new ventures once they've become part of your professional portfolio. Umfang: 01:24:13.00
Inhalt: A proper predictive analytics and data-mining project can involve many people and many weeks. There are also many potential errors to avoid. A "big picture" perspective is necessary to keep the project on track. This course provides that perspective through the lens of a veteran practitioner who has completed dozens of real-world projects. Keith McCormick is an independent data miner and author who specializes in predictive models and segmentation analysis, including classification trees, cluster analysis, and association rules. Here he shares his knowledge with you. Walk through each step of a typical project, from defining the problem and gathering the data and resources, to putting the solution into practice. Keith also provides an overview of CRISP-DM (the de facto data-mining methodology) and the nine laws of data mining, which will keep you focused on strategy and business value. Umfang: 01:28:06.00
Inhalt: 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 that produce business transformation. This course was designed to help you learn these, and other, nontechnical skills that can help you convert your first data science job into a successful, lifelong career. There are predictable challenges to be overcome when predictive models introduce change in organizations. Throughout this course, instructor Keith McCormick goes over these challenges and shows how to overcome them. Discover how to confidently defend your turf at work, enhance your own natural curiosity, deepen your commitment to your craft, effectively translate the language of analytics to the language of business, practice diplomacy, and more. Umfang: 00:44:15.00
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