Certified AI Workflow and Automation Specialist (CAWAS)

Explore artificial intelligence fundamentals, core principles, and how AI mimics human intelligence to perform tasks, plus its history, machine learning, neural networks, ethics, and applications across healthcare, finance, and entertainment.

Define artificial intelligence concepts and applications, from narrow AI to general AI, and explore practical AI workflows, tools like TensorFlow and PyTorch, and ethical deployment in industry.

Explore Nova's strategic ai integration to gain a competitive edge through narrow ai, ai-driven chatbots for customer service, explainable ai, data quality, and scalable cloud deployment.

The case study reveals an ai platform that analyzes patient data to predict health risks, personalize care, and boost hospital efficiency while addressing ethical and bias considerations.

Explore the core components of ai—machine learning, neural networks, and beyond—covering supervised, unsupervised, and reinforcement learning, with tools like scikit-learn, PyTorch, and TensorFlow for healthcare and finance.

Discover how Innovate X harnesses AI to transform healthcare, from radiology convolutional neural networks and predictive maintenance to reinforcement learning hospital workflows and nlp chatbots, with ethics and data privacy.

Explore current AI capabilities across machine learning, natural language processing, computer vision, and robotics, with tools like TensorFlow, NLTK, OpenCV, ROS, and PyTorch.

Tech Nova's AI transformation case study showcases data preparation, model training, and deployment using tools like pandas, scikit-learn, TensorFlow, and Docker, while addressing ethics and data privacy.

Explore ethical considerations and future implications of AI development, including bias and fairness, privacy, explainability, upskilling and reskilling, and environmental impact, with practical tools and frameworks.

Explore ethical and sustainable AI through a case study of Innovate AI, addressing bias against female candidates, privacy and explainability in Talent Match, with fairness techniques and green AI practices.

Explore the foundations and history of artificial intelligence, components like machine learning and neural networks, and applications in healthcare, finance, autonomous vehicles, natural language processing, computer vision, robotics, ethical considerations.

Master foundational AI workflows, frameworks, and data flow while covering data collection and preprocessing, model training, deployment, automation, and performance optimization.

Leverage an ai workflow to reduce readmission rates at Mercy General Hospital by integrating electronic health records and real time data streams with Apache Kafka, performing preprocessing and feature engineering.

Map data flow in ai systems with data flow diagrams and catalogs, and apply mvc architecture and mlops practices to optimize pipelines, privacy, and security.

Explore optimizing AI data flow in healthcare by mapping data pipelines, improving pre-processing, and ensuring governance and real-time monitoring for safer patient care.

Master the key stages of AI model development—from problem definition to maintenance—covering data preparation, model selection, training, evaluation, deployment, and monitoring with practical tooling.

A Fin Edge data scientist builds an AI driven credit risk model. The case study discusses problem framing, data privacy, ensemble methods, evaluation, deployment, and ongoing monitoring for concept drift.

Explore how automation enhances AI workflows through AutoML, data pre-processing, model training, and orchestration with Airflow and Kubeflow, ensuring data quality and fairness.

Explore automating ai workflows with AutoML and orchestration frameworks to boost DataX solutions efficiency, data pre-processing, and innovation.

Evaluate and optimize artificial intelligence workflow performance using Apache Airflow and MLflow, improving data preprocessing, deployment, and monitoring for scalable, accurate artificial intelligence systems.

Explore how Data Drive optimizes AI workflows using Apache Airflow and Bayesian optimization, improves data preprocessing and deployment via Jenkins CI/CD, and boosts churn model accuracy.

Master foundational frameworks for AI workflows, map data flow, and plan, design, train, evaluate, and deploy AI models. Automate processes and monitor performance to optimize value across applications.

Identify automation opportunities, evaluate processes for automation, and design, implement, and test simple automation workflows using scripting languages to boost productivity and reliability.

Discover automation concepts and tools that streamline workflows using RPA and AI integration, with practical examples from Zapier, UiPath, and the Automation Maturity Model.

Explore cargo line’s journey into strategic automation, boosting efficiency and innovation with rpa and Power Automate and UiPath while aligning with data security and cross-department collaboration.

Identify and analyze automation opportunities through process mapping and the RPA suitability framework to target repetitive, high-volume tasks and achieve cost, time, and accuracy improvements.

Case study Tech Nova's automation journey balances innovation with human centered strategies to redefine operational excellence through process mapping, robotic process automation, and ai powered support.

Design simple automation workflows by mapping processes, selecting tools like Zapier and UiPath, and defining triggers, actions, and conditions.

Map workflows with Lucidchart, implement automation using Zapier for simple tasks, and design an order-processing process to boost efficiency, cut data-entry errors, and align with strategic goals.

Learn automation with scripting languages like Python, JavaScript, and Ruby, reducing human error and boosting efficiency. Use Selenium and PyAutoGUI with Pandas for web, GUI, and data tasks.

Explore how Technova harnesses scripting languages, Python, Selenium, and Pandas, to automate web testing, data processing, and system monitoring, boosting enterprise efficiency and reliability.

Test and optimize automated processes using test automation frameworks, plan-do-check-act cycles, simulation environments, and data-driven analytics to improve efficiency, reliability, scalability, and security.

Apply ai-driven automation to optimize global logistics and cut delivery times and fuel use. Leverage pdca, selenium testing, and simulations for continuous improvement and robust, scalable operations.

Learn foundational automation concepts, tools, and scripting to streamline processes and identify opportunities. Design simple workflows, test, and optimize automated solutions for efficiency and accuracy.

Discover foundational machine learning concepts and applications across healthcare and finance, from supervised and unsupervised learning to linear regression and neural networks, and learn to evaluate performance while avoiding overfitting.

Master the fundamentals of machine learning, including supervised, unsupervised, and reinforcement learning. Apply practical workflows with Python, TensorFlow, PyTorch, data collection, feature engineering, training, and deployment.

TechNova applies supervised learning to smart city traffic management and environmental monitoring, using data collection, preprocessing, feature engineering, and TensorFlow, while addressing privacy, bias, and deployment challenges.

Explain supervised learning with labeled data for classification and regression, and unsupervised learning for clustering and dimensionality reduction, with real-world use cases in AI workflow automation.

Navigate supervised and unsupervised learning for enhanced customer engagement through personalized marketing. Explore data labeling considerations, feature engineering, and semi-supervised strategies to align with business goals.

Explore algorithms from linear regression to neural networks, including decision trees, random forests, SVMs, and convolutional neural networks, and learn practical tools for deploying machine learning in business.

Explore how linear regression gives way to decision trees, random forests, SVMs, and neural networks to predict traffic, optimize public transit, and drive urban planning insights.

Assess model performance with accuracy, precision, recall, and F1, using confusion matrices to diagnose errors. Employ cross-validation, regression metrics, bootstrapping, and ensemble methods with scikit-learn, TensorFlow, and PyTorch.

Explore how a data science team evaluates diabetes prediction models in healthcare, balancing accuracy, precision, recall, and F1 with confusion matrices, cross-validation, and ensemble methods to ensure reliable, safe predictions.

Explore strategies to reduce overfitting and underfitting while optimizing models with regularization, cross-validation, and hyperparameter tuning. Leverage ensemble methods, data augmentation, feature engineering, and regularization in TensorFlow and PyTorch.

Balance overfitting and underfitting in urban traffic prediction using regularization, cross-validation, feature engineering, and ensemble methods with historical, weather, and real-time GPS data.

Explore machine learning fundamentals, comparing supervised and unsupervised learning with labeled data and hidden patterns. Apply linear regression to neural networks and evaluate using accuracy, precision, recall, and f1 score.

Learn to locate and acquire data from structured databases and unstructured sources, clean and transform it, manage missing data and outliers, and integrate diverse data sets for insights.

Master data sources and acquisition techniques for structured and unstructured data, using MySQL, PostgreSQL, data lakes, web scraping, APIs, and real-time tools like Kafka.

Explore how Tech Nova optimizes data acquisition by balancing relational databases and data lakes, integrating structured and unstructured data with spark, kafka, and Scrapy, while ensuring privacy and regulatory compliance.

Master data cleaning to deliver accurate, consistent data for AI workflows, employing data profiling, missing data handling, standardization, validation, and automated routines with tools like OpenRefine, Pandas, and SQL.

Explore a case study of strategic data cleaning and robust data integration for healthcare data, highlighting data profiling, missing values, imputation, and standardization to improve integrity.

Structure data for analysis by cleaning, integrating, reducing, and transforming it to ensure consistency, accuracy, and readiness for machine learning and data-driven decision making.

Master data transformation across json, csv, and xml to unlock actionable retail insights, using data cleaning, imputation, outlier handling, schema matching, etl with nifi, and encoding for ml.

Handle missing data and outliers to improve artificial intelligence workflows. Apply deletion or imputation, including mean and multiple imputation, and detect outliers with z scores and interquartile range.

Integrate diverse data sets from databases, APIs, IoT, and social media using ETL, real-time data ingestion, and ontology-based approaches to enable actionable AI workflows and automated decision making.

Unify Titan Logistics data streams—fleets, gps, weather, and fuel data—into a platform with etl and cleansing. Leverage ontology, Protégé, Kafka, Spark, and governance for real-time decisions and reduced fuel use.

Explore end-to-end data preparation from identifying reliable data sources and acquisition context for structured and unstructured data, to cleaning, transforming, imputation, outlier detection, and data integration for accurate insights.

Explore neural networks from perceptrons to deep learning and learn how activation functions inject non-linearity to solve complex problems. Master backpropagation, optimization, and evaluation metrics to design architectures.

Explore neural network fundamentals, from perceptrons to deep learning, and learn practical tools like tensorFlow and PyTorch to build robust AI workflows.

Explore Alphatech's neural networks journey from perceptrons to deep learning for facial recognition, including CNNs and backpropagation. Learn about TensorFlow and PyTorch, overfitting prevention, activation choices, interpretability, bias.

Examine how activation functions shape neural network learning, from sigmoid and tanh to ReLU variants, boosting convergence and performance in tasks like image recognition and binary or multi-class classification.

Explains how a Datavision neural network uses data preprocessing, augmentation, and CNN design to improve early cancer detection, applying dropout, batch normalization, and evaluation on train, validation, and test sets.

Explore how backpropagation and optimization train convolutional networks for autonomous vehicle object recognition. The case study highlights Adam, skip connections, layer normalization, and transfer learning to boost efficiency and accuracy.

Evaluate neural network performance using metrics such as accuracy, precision, recall, F1, and ROC curves, plus confusion matrices and cross-validation. Leverage scikit learn, TensorFlow, and PyTorch for hyperparameter tuning.

Evaluate neural networks for customer churn by prioritizing recall, balancing precision and F1 score, and using confusion matrices, ROC and precision-recall curves to guide threshold tuning.

Explore neural networks from perceptrons to deep architectures, using activation functions like sigmoid, ReLU, and tan, and train with backpropagation and gradient descent to optimize precision, recall, and F1 score.

Explore natural language processing foundations, including text pre-processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text classification, to transform language data into actionable insights.

Master natural language processing concepts at the intersection of computer science, AI, and linguistics. Learn tokenization, stemming and lemmatization, POS tagging, word embeddings, and transformer models for practical NLP applications.

Explore how tokenization, stemming, lemmatization, and POS tagging, plus contextual embeddings and Bert transformers, drive sentiment analysis and advanced conversational agents for customer experience.

Master text pre-processing techniques and tools for NLP pipelines, including tokenization, stopword removal, stemming and lemmatization, normalization, and named entity recognition.

Case study shows transforming raw customer text into clean, structured data through tokenization, stopword removal, lemmatization, normalization, NER, and multilingual preprocessing to boost AI model performance.

Understand how tokenization converts characters into tokens to support NLP tasks like text analysis and sentiment analysis. Use NLTK, SpaCy, and transformers to handle punctuation, contractions, and multilingual tokenization.

Enhance clinical data analysis through tokenization with a customizable Spacy tokenizer tailored to medical jargon, improving preprocessing and accuracy in extracting diseases, symptoms, and treatments.

Master part of speech tagging and named entity recognition to transform raw text into structured data for NLP applications. Utilize NLTK, Spacy, and transformer models like Bert to automate workflows.

Master sentiment analysis and text classification basics in natural language processing. Use models from Naive Bayes to BERT and learn preprocessing, feature extraction, and sentiment detection.

Explore sentiment analysis and text classification through the DG markets case study, comparing Naive Bayes, SVM, and BERT, with pre-processing and cloud NLP tools.

Discover foundational natural language processing concepts, from text pre-processing and tokenization to named entity recognition, sentiment analysis, and text classification for scalable text analytics.

Explore the foundational concepts of robotic process automation and its benefits for enterprises. Learn to identify suitable processes, design robust, scalable workflows, and deploy RPA with planning to maximise ROI.

Learn how robotic process automation uses software bots to automate structured, rule-based tasks, boosting accuracy and cutting costs with UiPath and Automation Anywhere.

Case study shows how robotic process automation revolutionizes financial services by automating onboarding tasks with UiPath, reducing onboarding time from days to hours, and improving accuracy, compliance, and customer satisfaction.

Identify and analyze processes for automation with process mining and the automation potential matrix to prioritize high-benefit, low-complexity tasks, aided by governance and rpa-ai integration.

Explore how Innovate Tech uses process mining and an automation potential matrix to strategically implement RPA, integrate AI, and strengthen governance for sustained digital transformation.

Explore the key components and tools in RPA solutions, such as the recorder, development studio, runtime environment, and control center, and learn how bots automate repetitive processes.

Explore how Global Finance, Inc. uses robotic process automation to reduce loan processing times by automating data extraction, entry, and validation through a rigorous process assessment.

Design effective RPA workflows by mapping processes with Visio or Lucidchart, identifying repetitive tasks, selecting tools like UiPath, Blue Prism, Automation Anywhere, and emphasizing development, testing, and exception handling.

Map the purchase order workflow, choose UiPath, and implement a phased RPA with robust exception handling and ongoing monitoring aligned to strategic goals.

Learn best practices for implementing and scaling robotic process automation with governance, process mining, pilot projects, and change management to maximize ROI and scalability.

Learn foundational robotic process automation concepts and benefits, including automating repetitive tasks for cost efficiency and accuracy. Identify high-volume, rule-based processes, map workflows, and plan phased rollouts with stakeholder engagement.

Explore the landscape of ai tools and platforms, compare open source and commercial options, and learn integration, deployment, and tool selection to boost productivity and innovation in your workflows.

Explore the landscape of AI tools and platforms for workflow, automation, and decision making. Learn how TensorFlow, Azure AI, IBM Watson, and RapidMiner enable scalable deployment, security, and ethical compliance.

Trace TechNova's AI integration journey, evaluating TensorFlow, Azure AI, IBM Watson, and RapidMiner to boost scalability and customer satisfaction while upholding data governance, ethics, and GDPR with Crisp-DM.

Explore the key features and capabilities of leading AI platforms like Google Cloud AI, Azure AI, IBM Watson, and AWS, with tools like CRISP-DM and AutoML.

This case study shows how Technova uses AI platforms to automate workflows and enhance decision making with Google Cloud AI, Azure AI, IBM Watson, and AWS AI, plus AutoML.

Compare open source and commercial tools for artificial intelligence to weigh flexibility, cost, and support, with examples like TensorFlow, PyTorch, Azure, and SageMaker.

Explore how Technova weighs open source versus commercial AI tools to balance innovation, cost, security, and scalability, illustrated by case studies on Apache Airflow, SageMaker, and PyTorch.

Master integration and deployment strategies for AI solutions using APIs, data transformation, cloud or on-prem deployment, containerization, and CI CD to deliver scalable, reliable AI.

Examines Innov AI's chatbot integration with a CRM, evaluating RESTful APIs, data transformation with Kafka, and privacy concerns. Builds with containerization, Kubernetes, CI/CD, monitoring to balance performance and cost.

Identify your workflow needs and evaluate AI tools for compatibility, scalability, cost, usability, and security, using pilots and case studies to select the best solution for your operations.

Optimize AI tool selection for Technova's supply chain by matching capabilities to inventory management and demand forecasting, with cost-benefit analysis guiding integration and scalability decisions.

Explore the landscape of AI tools and platforms, compare open source and commercial options, and learn how to integrate and deploy AI solutions to align with business goals.