AI Consulting Mastery: Generative & Predictive AI in 10 Days

Map three AI paradigms: supervised, unsupervised, and reinforcement learning—and learn when to apply each to business challenges like churn, market segments, and routing optimization.

Unsupervised learning discovers structure in raw data without labels, clustering and revealing patterns and dimensions, with models like k-means, PCA, and autoencoders for customer segmentation and anomaly detection.

Examine reinforcement learning and the three learning paradigms: supervised, unsupervised, and reinforcement. Learn how to frame problems, select data, and guide strategy in AI consulting.

Discover how deep learning and transformer architectures move AI from pattern recognition to language understanding, covering CNNs, RNNs, LSTMs, attention, and embeddings with business implications.

Explore CNNs for visual data in healthcare and manufacturing, RNNs and LSTMs for time-based insights, and transformers for language and multimodal applications like chatbots and summaries.

Explore key ai techniques for consultants, including regression, decision trees, neural networks, clustering, pca, autoencoders, and reinforcement learning, and learn to match methods to business problems.

Connect APIs, embeddings, and data pipelines to turn raw data into actionable insights and business value, guided by questions on data sources, updates, and embeddings.

Master moving ML from prototype to production with MLOps, governance, and drift monitoring, using CI/CD and retraining policies to ensure reliable, transparent AI systems.

Explore how large language models power generative ai with transformers, pre-training, and fine tuning, and learn to translate their capabilities into client value.

Assess the capabilities, limitations, and business implications of large language models, including multilingual reasoning, creativity, hallucinations, context window limits, bias, and governance for responsible AI adoption.

Leverage retrieval augmented generation (RAG) to ground LLMs with enterprise data, reducing hallucinations and enabling verifiable, auditable outputs through embeddings, vector stores, and governance.

Master prompt engineering and context optimization to craft precise prompts and guide AI toward reliable outputs. Use role, task, format, constraints, and zero-shot and few-shot approaches to ensure repeatable results.

Explore context optimization to manage memory in limited context windows, curate essential dialogue, and leverage retrieval, prompt management, and evaluation frameworks for auditable, reliable ai deployments.

Reduce hallucinations and boost reliability in client LMS work by applying design, evaluation, and human in the loop strategies, grounding outputs, and using retrieval-based mechanisms and guardrails.

Design, evaluate, and deploy chatbots and conversational interfaces that boost customer experience, reduce costs, and maintain trust, grounding with a memory-enabled llm brain, knowledge base, apis, and a feedback loop.

Examine real-world chatbots and conversational interfaces with multilingual, retrieval-grounded LLMs in banking and internal IT and HR. Measure impact with KPIs, ROI, faster responses, and user trust.

Leverage LMS to automate content generation while preserving brand voice and compliance across multilingual channels. Implement governance with prompts and human-in-the-loop reviews to ensure consistency, accuracy, and measurable outcomes.

Examine real-world content generation cases where AI and humans align brand tone and multilingual messaging via prompt templates, governance dashboards, and a retrieval augmented GPT pipeline.

Learn how to advise on and implement AI coding assistants to boost developer productivity, quality, and compliance through tools like GitHub Copilot and code search.

Learn how to deploy AI in legally regulated sectors with a focus on accuracy, compliance, and auditability, covering legal, financial, and healthcare use cases and explainability for trust.