{"product_id":"ai-for-software-engineers-concepts-and-techniques","title":"AI for Software Engineers: Concepts and Techniques","description":"\u003cdiv\u003e\u003cp\u003eThis three-day intensive course is designed to equip software engineers, project managers, and technical leads with the tools and insights needed to leverage artificial intelligence (AI) effectively. By focusing on AI concepts, practical implementations, and ethical considerations, participants will enhance their ability to integrate AI into modern software projects.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eAI for Software Engineers: Concepts and Techniques Benefits\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cb\u003eFoundation\u003c\/b\u003e: Understand core AI concepts and their integration with software engineering.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003ePractical Tools\u003c\/b\u003e: Use AI tools for testing, debugging, and project management.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eEthics\u003c\/b\u003e: Explore responsible and ethical AI practices.\u003c\/li\u003e\n\u003cli\u003e\n\u003cb\u003eHands-On\u003c\/b\u003e: Apply concepts through practical labs addressing real-world challenges\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cb\u003ePrerequisites:\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eProficiency in \u003cb\u003ePython\u003c\/b\u003e\n\u003c\/li\u003e\n\u003cli\u003eFamiliarity with \u003cb\u003eSDLC fundamentals\u003c\/b\u003e (version control, CI\/CD, agile methodology)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eAI for Software Engineers: Concepts \u0026amp; Techniques Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cb\u003eDay 1: AI Foundations and Basic ML Concepts\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 1: Introduction to AI in Software Development\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eAI vs. Conventional Systems\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eNarrow, General, and Super AI\u003c\/li\u003e\n\u003cli\u003eAI hardware (GPUs, TPUs) and popular frameworks\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eAI in the SDLC\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eBenefits (automation, predictive insights) and risks (maintenance, data quality)\u003c\/li\u003e\n\u003cli\u003eAI as a Service (AIaaS) and service contracts\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 2: Quality Characteristics and Ethics in AI\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eKey Quality Factors\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eFlexibility, Adaptability, Autonomy\u003c\/li\u003e\n\u003cli\u003eTransparency \u0026amp; Explainability\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eEthical \u0026amp; Regulatory\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eBias, Reward Hacking, and compliance (e.g., GDPR)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eRisk Management\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eIdentifying and mitigating biases\u003c\/li\u003e\n\u003cli\u003eDocumentation of AI components\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 3: Machine Learning Overview\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eML Types\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eSupervised, Unsupervised, Reinforcement Learning\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eML Workflow\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eData collection, preprocessing, model training, evaluation, deployment\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eOverfitting \u0026amp; Underfitting\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eCauses, detection, and mitigation (e.g., regularization)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 1: Overfitting \u0026amp; Underfitting (Titanic Dataset)\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003e\u003cb\u003eScenario: Predict passenger survival on the Titanic.\u003c\/b\u003e\u003c\/li\u003e\n\u003cli\u003eGoal: Demonstrate how model complexity influences performance.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eLoad and preprocess Titanic data.\u003c\/li\u003e\n\u003cli\u003eTrain multiple classifiers (e.g., logistic regression vs. random forest).\u003c\/li\u003e\n\u003cli\u003eObserve overfitting\/underfitting effects on validation accuracy.\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eDay 2: Data Handling, Regression, and Prompt Engineering\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 4: Data Preparation \u0026amp; Handling\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eData Quality\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eCleaning, handling missing values, outliers, categorical features\u003c\/li\u003e\n\u003cli\u003eTrain\/validation\/test splits\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eCommon Pitfalls\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eImbalanced classes, mislabeled data, domain knowledge gaps\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 2: Data Preparation (NYC Taxi Dataset)\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eScenario: Forecast taxi fares in NYC (regression).\u003c\/li\u003e\n\u003cli\u003eGoal: Clean a real-world dataset and create meaningful features.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eLoad NYC Yellow Cab data (pickup\/dropoff times, distances).\u003c\/li\u003e\n\u003cli\u003eHandle missing data and detect outliers.\u003c\/li\u003e\n\u003cli\u003eEngineer features (e.g., time-of-day, trip distance).\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 5: Model Evaluation Metrics\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eRegression Metrics\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eMSE, RMSE, MAE, R²\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eClassification Recap\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eAccuracy, precision, recall, F1-score, confusion matrix\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eChoosing the Right Metric\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eContextual needs (business value, safety-critical)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 3: Regression Modeling (NYC Taxi Fares)\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eScenario: Build and evaluate models to predict fare amounts.\u003c\/li\u003e\n\u003cli\u003eGoal: Compare linear regression vs. gradient boosting to measure error rates.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eTrain at least two regression models on NYC Taxi data.\u003c\/li\u003e\n\u003cli\u003eCompute MSE, RMSE, and MAE on the validation set.\u003c\/li\u003e\n\u003cli\u003eDiscuss feature importance and next steps.\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 6: Prompt Engineering for Generative AI\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003ePrompting Best Practices\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eRole-based prompting, zero-shot vs. few-shot, chain-of-thought reasoning\u003c\/li\u003e\n\u003cli\u003eStructuring prompts for clarity, constraints, and context\u003c\/li\u003e\n\u003cli\u003eIterative refinement (synonyms, repeated keywords, output format)\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 4: Designing a Sophisticated Prompt for Software Engineering\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eScenario: Generate detailed, actionable advice on software architecture, testing, or refactoring in a microservices environment.\u003c\/li\u003e\n\u003cli\u003eGoal: Apply advanced prompting techniques (role prompting, constraints, few-shot examples) to create a high-quality prompt that yields expert-level recommendations.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eDefine Context \u0026amp; Role (e.g., “You are a principal software architect…”).\u003c\/li\u003e\n\u003cli\u003eProvide Examples (show how you want the answer structured or styled).\u003c\/li\u003e\n\u003cli\u003eAdd Constraints (limit response length, include specific bullet points).\u003c\/li\u003e\n\u003cli\u003eIterate \u0026amp; Refine (test and adjust wording for clarity \u0026amp; precision).\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eDay 3: Neural Networks, Explainability, and Responsible AI\u003c\/b\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eModule 7: Neural Networks Introduction\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eNN Basics\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003ePerceptrons, hidden layers, activation functions\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eNN Use Cases\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eImages, text, speech; large-scale data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eTesting Neural Networks\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eSpecial considerations, coverage measures\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 5: Neural Network Classification (MNIST)\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eScenario: Classify handwritten digits from MNIST.\u003c\/li\u003e\n\u003cli\u003eGoal: Implement and train a feed-forward neural network.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eLoad MNIST images (28x28).\u003c\/li\u003e\n\u003cli\u003eBuild a simple network (e.g., feed-forward).\u003c\/li\u003e\n\u003cli\u003eEvaluate accuracy and discuss improvements (layers, dropout, etc.).\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 8: Testing \u0026amp; Model Explainability\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eLevels of Testing\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eInput data testing, model testing, system \u0026amp; acceptance testing\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eAdversarial Attacks \u0026amp; Data Poisoning\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eDefenses, monitoring strategies\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eExplainability Methods\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eLIME, SHAP, local vs. global interpretation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eLab 6: Model Explainability (U.S. Housing with LIME)\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eScenario: Stakeholders want insights into house pricing predictions.\u003c\/li\u003e\n\u003cli\u003eGoal: Use LIME to explain predictions of a regression model.\u003c\/li\u003e\n\u003cli\u003eLab Steps:\u003c\/li\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eTrain a regression model on a U.S. housing dataset (e.g., Ames Housing).\u003c\/li\u003e\n\u003cli\u003eApply LIME to interpret specific predictions.\u003c\/li\u003e\n\u003cli\u003eIdentify potential biases or anomalies in model behavior.\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eModule 9: Responsible AI \u0026amp; Wrap-Up\u003c\/b\u003e\u003c\/p\u003e\n\u003cul type=\"disc\"\u003e\n\u003cli\u003eGovernance \u0026amp; Compliance\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003ePrivacy, fairness, disclaimers, accountability\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eFuture Trends\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eLarge Language Models (LLMs), multi-modal AI, MLOps\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cli\u003eKey Takeaways\u003c\/li\u003e\n\u003cul type=\"circle\"\u003e\n\u003cli\u003eData and model versioning, transparency, bias mitigation, robust QA\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/ul\u003e\n\u003cdiv align=\"center\"\u003e\u003chr align=\"center\" width=\"100%\" size=\"1\"\u003e\u003c\/div\u003e\n\u003cp\u003e\u003cb\u003eSummary of Labs\u003c\/b\u003e\u003c\/p\u003e\n\u003col type=\"1\" start=\"1\"\u003e\n\u003cli\u003eLab 1: Overfitting \u0026amp; Underfitting (Titanic)\u003c\/li\u003e\n\u003cli\u003eLab 2: Data Preparation (NYC Taxi)\u003c\/li\u003e\n\u003cli\u003eLab 3: Regression Modeling (NYC Taxi Fares)\u003c\/li\u003e\n\u003cli\u003eLab 4: Designing a Sophisticated Prompt for Software Engineering (GenAI)\u003c\/li\u003e\n\u003cli\u003eLab 5: Neural Network Classification (MNIST)\u003c\/li\u003e\n\u003cli\u003eLab 6: Model Explainability (U.S. Housing with LIME)\u003c\/li\u003e\n\u003c\/ol\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"268A42US \/ 2026-08-05T09:00:00 \/ Herndon, VA","offer_id":47534221197531,"sku":"US-1851-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"26BB36US \/ 2026-11-04T09:00:00 \/ Herndon, VA","offer_id":48216565088475,"sku":"US-1851-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"272A89US \/ 2027-02-03T09:00:00 \/ Herndon, VA","offer_id":48216565121243,"sku":"US-1851-IL","price":2228.0,"currency_code":"USD","in_stock":true},{"title":"275B23US \/ 2027-05-05T09:00:00 \/ Herndon, VA","offer_id":48741611667675,"sku":"US-1851-IL","price":2228.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/learningtreeinternational-dirinfosec-hhs.myshopify.com\/products\/ai-for-software-engineers-concepts-and-techniques","provider":"Learning Tree International","version":"1.0","type":"link"}