Responsible AI/ML Masterclass 2.0 (including Gen AI)

THE MOST COMPLETE COURSE ON RESPONSIBLE AI, PERIOD

AI is moving faster than most people's ability to govern it responsibly. Every week brings a new model, a new regulation, a new failure mode nobody had a name for six months ago. Most "responsible AI" courses pick one slice of this problem: a course on fairness, a course on explainability, a course on the EU AI Act... and leave you to stitch the rest together yourself.

This course is different. It's the complete, end-to-end map: from the data issues that quietly corrupt a model's decisions, to the way generative AI explains (or fails to explain) itself, to the regulations now enforcing all of it. If you want one course that actually covers everything responsible AI touches, this is it.

No exaggeration - this course will contain everything about AI that you may possibly think of.

• Diagnosing and treating biased data? Check;

• Mapping regulatory obligations to model use cases, fairness scores, and/or controls? Check;

• Identifying and dealing with training problems from underfit to overfit, learning noise, irrelevant correlations and others - and how to treat them? Check;

• How to explain model decisions - both in discriminative models with local and global attribution, surrogates, concepts, gradients, and more, but also generative models, with evidence sources such as RAG, tool logs, reasoning chains and more? Check;

• How to deal with "human" part of AI models, including overrides, Gen AI output validation and what to prioritize, and more? Check;

Trust me when I say that this 22-hour masterclass tackles responsible AI in any way, shape or form that you may think of. Generative or discriminative; black box or white box; open-weight or closed-weight; simple or complex; It's all here.

WHAT'S INSIDE

The course runs across 12 modules. We start with the foundations: what AI actually is, how discriminative and generative systems learn and fail differently, and the core principles and governance structures that keep AI in bounds. From there, we dig into the issues hiding in your data (proxies, bias, feedback loops) and your models (adversarial sensitivity, overfitting, leakage, drift), then flip to the responsible practices that prevent them, across data handling, model training and deployment, and post-deployment usage.

The back half goes deep on two areas most courses treat as an afterthought. First, explainability: how it works, where it breaks, and how it fundamentally differs between discriminative models and generative AI, including the faithfulness gap in chain-of-thought reasoning and how to read mechanistic evidence. Second, ethics, fairness, and regulation: the duties you owe when you build and deploy AI, the new risks generative AI introduces like deepfakes and IP exposure, and how the EU AI Act, GDPR, CCPA, and long-standing fairness law all apply.

LET ME TELL YOU... EVERYTHING

Here's the full list of what's covered. No vague chapter titles, the actual granular content. Some people just like to ask, "Hey, what is everything I will learn?". Well, here it is:

• You will learn what AI is, from terminology to the full AI lifecycle;

• You will learn the characteristics of discriminative AI, including how it decides, learns, and fails;

• You will learn the characteristics of generative AI, including how it builds answers and where it fails;

• You will learn to identify AI issues and risks, from biased data to machine-scale harm;

• You will learn the core responsible AI principles and how AI governance and risk management keep systems in bounds;

• You will learn the regulatory landscape overview: why AI is regulated and what the laws demand;

• You will learn how reduced and overloaded/correlated features can blind or distort a model;

• You will learn how proxies detach features from reality and reassemble sensitive attributes from "harmless" fields;

• You will learn how historical and characteristic biases cause models to replicate unfairness and degrade performance for underrepresented groups;

• You will learn how defaulting, overefficiency, and feedback loops quietly corrupt a model's outputs and metrics over time;

• You will learn adversarial sensitivity, and how tiny targeted inputs make models fail confidently;

• You will learn overfitting, data leakage, data loss, and drift, and why accurate models quietly go wrong over time;

• You will learn data quality and ethical data quality as distinct dimensions, not one global score;

• You will learn data retrieval quality, input/output sanitization, and guardrails across the data lifecycle;

• You will learn data profiling, rebalancing, parsing, and standardization to correct skew and messiness;

• You will learn retrieval chunking, metadata, data provenance, lineage, privacy, and security;

• You will learn adversarial robustness and proper feature selection for responsible model training;

• You will learn Gen AI security and hallucination reduction techniques specific to generative systems;

• You will learn specialist combinations and Mixture of Experts (MoE) routing, and automation/agentic orchestration;

• You will learn Gen AI prompt optimization and context window optimization as responsible design choices;

• You will learn how to evaluate whether a model is acceptable to deploy for a specific use case;

• You will learn model evaluation beyond a single accuracy score, and output fairness validation across groups;

• You will learn how to prevent and propagate errors deliberately, and when human overrides are worth their cost;

• You will learn what makes AI explanations useful (not just accurate), and how transparency, interpretability, and explainability differ;

• You will learn to design for explainability from the start, and how methods differ between discriminative and generative AI;

• You will learn feature importance, local attribution, counterfactual, gradient-based, concept-based, and global surrogate explainability methods for discriminative AI;

• You will learn the faithfulness gap in generative AI: why a fluent explanation isn't proof of how a model reached its answer;

• You will learn to read reasoning-based explanations, calibrate confidence, and resolve contradictions in generative AI outputs;

• You will learn to ground generative AI claims in checkable evidence: sources, tool call actions, and full agent trajectories;

• You will learn mechanistic interpretability methods that open the network to read its real internal computations;

• You will learn consent and data authority, data retention, surveillance, and AI usage disclosure and transparency;

• You will learn AI governance, ownership, accountability, enforcement, and the socioeconomic impact of AI at scale;

• You will learn critical thinking and thought delegation: which mental work is safe to hand to AI;

• You will learn hallucination risk strategy, targeting controls where being wrong costs the most;

• You will learn synthetic media, deepfakes, IP and copyright issues in training data, and how Gen AI scales misinformation and manipulation;

• You will learn the EU AI Act's risk tiers and deadlines, and why it applies even to companies without a European presence;

• You will learn the US policy landscape, GDPR, CCPA, and how long-standing fairness law reaches AI in finance, housing, and hiring;

MY INVITATION TO YOU

Remember that you always have a 30-day money-back guarantee, so there is no risk for you.

Also, I suggest you make use of the free preview videos to make sure the course really is a fit. I don't want you to waste your money.

If you think this course is a fit and can take your responsible AI/ML model knowledge to the next level... it would be a pleasure to have you as a student.

See you on the other side!