Web security: Injection Attacks with Java & Spring Boot

The AI Literacy Specialization Program is one-of-a-kind hierarchical cognitive skills based curriculum that teaches artificial intelligence (AI) based on a scientific framework broken down into four levels of cognitive skills.Part 2: Use Apply combines the below two cognitive skills -Using (practicing AI concepts in realistic environments)Applying (adapting AI knowledge to solve real-world problems)This part of the program emphasizes practical implementation and hands-on skill-building through structured exercises and applied use cases. It includes 3 core competencies, each supported by detailed performance indicators, totaling 20. These are designed to ensure learners are able to confidently navigate and apply AI technologies in varied contexts.Competency Overview1) Traditional AIThis competency focuses on foundational AI methods developed before the deep learning era and includes core machine learning approaches. Learners will understand the end-to-end AI workflow and the different layers involved in building traditional AI systems.Performance Indicators:Understanding the AI Technology StackApplication Layer: User interface and business application logicModel Layer: Machine learning algorithms and training logicInfrastructure Layer: Cloud platforms, hardware accelerators, and deployment toolsCommon Components: Data pipelines, model monitoring, and governanceChoosing the Right Tech Stack for Business Use CasesEnd to end Use Cases:Credit Card Default PredictionHousing Price PredictionSegmentat

Master course in data science and business analytics 3.0Data scienceHey there! Ever wondered how data scientists work their magic? Well, data science is like a superpower that helps us dig out precious gems of knowledge from all sorts of data. It's a mix of statistical analysis, machine learning, data visualization, and computer programming that helps us make sense of complex data sets.The ultimate goal of data science is to uncover hidden patterns, trends, and connections within data, so we can make smart decisions based on solid insights. It's like detective work, but with data as our crime scene.To crack the case, data scientists follow a series of steps. They start by collecting data from different sources and then clean and prepare it for analysis. Next, they dive deep into the data, exploring its secrets and relationships.But data science doesn't stop there! It finds its applications in various fields like business, finance, healthcare, marketing, and social sciences. In today's data-driven world, organizations are racing to leverage their data assets to gain an edge, optimize their processes, and make informed decisions.So, the next time you hear about data science, remember it's the superhero behind the scenes, unveiling the insights that shape our world.Ever wondered how businesses make those smart decisions? Well, that's where business analytics comes into play. It's like a secret weapon that uses data analysis and statistical methods to unlock valuable insights and help businesses make informed choices.Business AnalyticsBusiness analytics is all about crunching numbers and analyzing past performance to understand what worked and what didn't. It involves collecting, cleaning, and modeling data to reveal trends, patterns, and even predict future outcomes. It's like having a crystal ball for business success!But it doesn't stop there. Business analytics covers a wide range of activities, from visuali

You want to pass AWS Certified Solution Architect ? If yes then this course is for You ! Do you know because of Amazon AWS Jeff Bezos is the Richest Person of the World! So think about the opportunity do you have if you know AWS. According to Glassdore the average salary of AWS Certified Solution Architect is $119,233. So it is a good time to grab the opportunity! In This Course We Offer: *** 17 Hours Of On Demand Video Tutorial *** *** 1000+ MCQ Question ****** 100+ Study Materials *** *** 1000+ AWS Certified Solution Architect Question *** What Is In this course about? Welcome to AWS certified solution Architect course. The course is specially designed for the newcomer who is planning to enter the field of cloud computing. No programming knowledge needed and no prior AWS experience required for this course.Nowadays AWS is a must have for IT professional. If you are serious about Amazon web service then the course will guide you. This course is covered all the basic fundamental of AWS everything you need to know about Cloud, cloud service Model, EC2, Amazon simple storage service, Amazon Glacier, Advantage of Cloud computing, Amazon virtual private cloud, Amazon Elastic load Balance, Network Load Balancer, Classic Load Balancer and Auto Scaling. If a candidate never logged by this AWS platform before, at the end of our AWS training he will be able or qualified to take the AWS certified solutions Architect exam. At the end of this course, you will gain in depth knowledge of AWS solution architect and general AWS skill to help your company or your project. With this AWS certificatio

Unlock your path to AWS Certified Machine Learning – Specialty (MLS-C01) mastery with this comprehensive, hands-on course! Perfect for data scientists, ML engineers, and AWS professionals aiming to certify and build production-grade ML solutions on AWS.This course covers all four official exam domains with up-to-date content aligned to the latest guide:Data Engineering (20%): Create scalable data repositories, implement ingestion pipelines (Kinesis, Firehose, Glue, EMR), orchestrate batch/streaming workloads, and transform data for ML using AWS Glue, Spark, and efficient storage (S3, EFS, databases).Exploratory Data Analysis (24%): Sanitize datasets, handle missing values/outliers/imbalance, perform feature engineering with SageMaker Data Wrangler, and visualize/analyze data to uncover insights and detect bias early using SageMaker Clarify.Modeling (36%): Frame business problems as ML tasks, select/train/tune models with built-in algorithms (XGBoost, DeepAR, BlazingText, Image Classification, Factorization Machines), apply distributed training, hyperparameter optimization, evaluate performance, and ensure explainability via SHAP and Debugger rules.Machine Learning Implementation and Operations (20%): Deploy models (real-time endpoints, batch transform, multi-model, serverless), implement MLOps with SageMaker Pipelines and Model Registry, monitor drift/quality/bias (Model Monitor), secure workloads (VPC isolation, encryption, IAM), optimize costs (Spot instances, autoscaling), and maintain reliable production ML systems.Featuring practical labs, Python code examples, real-world scenarios, 100+ exam-style MCQs, and step-by-step SageMaker workflows, you'll gain the skills to pass the MLS-C01 exam confidently before the deadline. Whether advancing your career in AI/ML, validating expertise in SageMaker/MLOps, or preparing for hi

Artificial Intelligence is everywhere — but many people still struggle to use it effectively in real situations.This course shows you a clear and practical way to work with GPT, without technical complexity or unnecessary theory.Instead of focusing on tools alone, you will learn how to think and work with AI in a structured way.You will understand how GPT generates results, why those results can sound convincing, and how to interpret them correctly.From there, the course moves into practical application.You will learn how to structure information, handle complex inputs, and turn unclear situations into clear outcomes.You will also see how GPT can support everyday tasks such as writing, decision-making, learning, and organizing information.A key focus of this course is developing a repeatable working method.You will learn how to:build context effectivelywork in clear stepsimprove results through iterationmaintain control over decisionsIn the final part, you will go one step further and create your own GPT assistant.You will learn how to define tasks, structure workflows, and build assistants that support you in recurring activities.This course is designed for beginners who want to move beyond basic prompting and use AI in a more structured, practical, and efficient way.By the end of the course, you will not only know how to use GPT —you will understand how to work with it.

Kubernetes is an open-source platform for managing containerized applications across multiple hosts. It offers a rich set of features that facilitate application deployment, scaling, and operations, and this makes it one of the hottest topics right now.By taking this course you will learn Kubernetes concepts beyond the basics such as environment variables, ConfigMaps, Secrets, DaemonSets and more with hands-on labs.You will learn how to store data in ConfigMaps and then inject them to pods for setting environment variables, or by mounting them as volumes. We learn also about Secrets in Kubernetes, which is the right place for storing sensitive data.You will discover also alternative mechanisms for deploying pods in Kubernetes, aside from using deployments, which can be the right solution for certain use casesRequirements for this Course:Basic knowledge of Kubernetes such as Yaml, pods, ReplicaSets, Deployments Who this course is for:Anyone who wants to enhance their skills in orchestrating containers with Kubernetes.Those who are already familiar with the basics of Kubernetes and want to learn more advanced Kubernetes concepts.What you’ll learnHow set environment variables in Kubernetes?How to create ConfigMaps?How to use ConfigMaps for setting environment variables?How to mount ConfigMaps as volumes?How to create Secrets?How to use Secrets for setting environment variables?How to mount Secrets as volumes?deploying pods using DaemonSetsDeploying pods using StatefulSetsWorking with Jobs and CronJobs in Kubernetes