At the core of predictive analytics lie the models used to make predictions after the data has been collected and preprocessed. Explore predictive techniques, including A/B testing, Bayesian Networks, and the support vector machine (SVM).

The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Discover how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.

Predictive analytics involves widely accepted tools and techniques that enable organizations to make informed decisions regarding potential future events. Examine how correlation and regression are employed in predictive analytics.

Most data that organizations collect doesn't offer much value. However, by applying the right techniques, you can extract powerful insights from the stockpile of data. Discover data collection and exploration for best possible prediction.

Predictive analytics involves a wide range of statistical tools and methods that allow an analyst to build a powerful predictive model. Explore the importance of statistics and probability theory in predictive analytics.

Purposeful information can be extracted from large data sets to determine what has, could, or should happen. Explore descriptive, predictive, and prescriptive analytics, including data mining, distribution models, and hypothesis testing.

Predictive analytics delivers the greatest value when the data being modeled is relevant to the business goals. Examine the preprocessing phase of data collection to provide the best predictive model.

With predictive analytics, relevant data should be stored for easy retrieval and kept up-to-date, with attributes selected contingent on their predictive potential. Explore data reduction and graphic tools for exploratory data analysis.

Kubernetes Service defines a logical set of pods, a policy to access them and provide efficiency to the microservices deployed in the clusters. Kubernetes networking uses iptables to manage network connections between pods and nodes to enable communication across Kubernetes cluster components. In this course you'll investigate the Kubernetes Network model, the technologies that can be used to implement the Kubernetes Networking model, the challenges of pod networking, how services can help mitigate the challenges and why proxying is used for services. You'll recognize the features of the prominent types of Kubernetes service, the role of EndpointSlices and the supported AddressTypes. Next, you create a network namespace and list all the available namespaces, creates two HTTP server pods and verify the pods are running, create a service without a Pod selector, manually map the service to the network address where it's running and configure multiple port definitions on a service object. Finally, you'll create a configuration file to configure type NodePort and type LoadBalancer, create a deployment that runs 3 replicas of an application and create an internal TCP LoadBalancer using a service. This course is part of a series that aligns with the objectives for the Certified Kubernetes Administrator exam and can be used to prepare for this exam.

An architecture is considered resilient if it is continuously operational and can sustain failures. Kubernetes high availability is all about setting up Kubernetes, along with its supporting components, in a way that leaves no single point of failure, and has the capability to detect hardware or software faults and remediate them. In this course, you'll learn the Kubeadm commands and flags that can be used to manage, bootstrap, and join Kubernetes clusters. You'll explore the highly-available Kubernetes architecture, the benefits of multi-master HA architecture, and the advantages and disadvantages of approaches for setting up HA Kubernetes clusters. Next, you'll investigate the stacked and external etcd topologies, the role of etcd in Kubernetes, and the concepts of leaders and elections. You'll learn about the essential control plane components and how to back up etcd clusters and use them to recover Kubernetes clusters. You'll examine how to create a load balancer for kube-apiserver and add control plane nodes to it, initialize a stacked control plane, and join multiple stacked control plane nodes. You'll discover how to set up HA clusters with external etcd nodes, add additional control planes to the clusters, install workers after bootstrapping a control plane, and finally take snapshots using etcdctl commands and use the snapshots to restore clusters. This course is part of a series that aligns with the objectives for the Certified Kubernetes Administrator exam and can be used to prepare for this exam.

Regression modeling investigates relationships between dependent and independent variables and is heavily relied upon for predictive analytics and data mining applications. Explore both the linear and logistic regression models.

Both supervised and unsupervised machine learning techniques are at the forefront of the predictive analytics and data mining industry. Discover machine learning features and tools, propensity scoring, and segmentation modeling.

Analytic model management ensures that models are not only superior to alternatives, but they also meet or exceed current business needs. Examine the process of building, validating, and evaluating a predictive analytics model.

Analysts must continuously manage analytical models, such as monitoring performance over time and interacting with various stakeholders. Explore the operational decision-making stages of model life cycle management.

Predictive analytics uses techniques, such as statistics and machine learning, to build predictive models, often using big data to test and validate these models. Explore key features of predictive analytics and big data.

Nestled within machine learning are ensemble techniques, enabling the combination of multiple models to reduce prediction error and improve forecasting ability. Examine machine learning methods, including random forests and uplift models.

Text mining facilitates social network analysis, giving analysts the ability to capture people's sentiments about various topics. Examine how text mining and social network analysis can greatly impact many diverse areas.

Time series modeling is a common forecasting method, such as making stock market predictions. It has made its way into many varied applications, including inventory management and healthcare. Explore the features of time series modeling.