“I use an AI assistant daily, but I cannot say why it gets things wrong.”
The answer reads well, so you paste it in. Sometimes a figure or a reference turns out to be invented, and you have no way of telling in advance which answers to check.
You have used an AI assistant. This course takes you from there to an understanding you can defend: how models learn, how generative AI produces an answer, where both fail, and how to use them responsibly at work. About 12 hours, self-paced, then a certification exam.
AI/ML Foundations is a self-paced online AI and machine learning foundations course from Bodhih Training Solutions, Bengaluru. It is Level 1 of the Bodhih AI/ML certification and needs no coding. Over about 12 hours you take a diagnostic, study six modules covering machine learning basics, data, model evaluation, generative AI, responsible use and AI at work, sit two mock exams, and then sit a 60-question certification exam under exam conditions. Passing earns a verifiable credential.
The answer reads well, so you paste it in. Sometimes a figure or a reference turns out to be invented, and you have no way of telling in advance which answers to check.
Training data, overfitting, precision, hallucination, retrieval. Colleagues and vendors use these terms freely, and you are approving budgets or plans that depend on them.
Customer data, a contract, source code, a salary sheet. Your organisation may have an AI policy, but nobody has walked you through what it means for the work on your desk.
People in operations, HR, finance, sales or support who want AI explained properly, without Python and without being talked down to.
Technical staff who have not studied machine learning formally and want the shared vocabulary before they start building.
People who choose use cases, brief data or ML teams, and sign off on work that an AI system helped produce.
Organisations that want every employee to hold the same examined baseline. Seats are ₹7,500 per seat for companies.
If you already build and evaluate models in Python and want hands-on labs and a reviewed project, this level will feel too basic: look at Level 2, Certified Applied AI/ML Practitioner, instead.
A course records that you watched. This records that your understanding was examined: one domain at a time, with a pass mark in every one. These are the six abilities the certification exam tests, and the credential names them.
You can explain the kinds of learning, where machine learning fits among ordinary software, and what it cannot do, in words a colleague would follow.
You can ask the right questions about data quality, labelling, bias, privacy and splits, and explain why a model inherits the faults of what it was trained on.
You can explain training, overfitting and baselines, and say why accuracy alone is often the wrong number to trust.
You can describe how an LLM generates an answer, write prompts that work, recognise hallucination, and explain retrieval in one picture.
You can weigh risk, fairness, intellectual property and confidentiality, keep a human in the loop, and read your organisation’s AI policy closely.
You can choose a sensible use case, read a model’s output critically, and brief and question a data or ML team.
The list below is the live programme: a diagnostic, six modules, two mock exams and the certification exam. It takes about 12 hours over roughly 4 weeks at your own pace. As you read the module list, notice which topics you already use at work without being able to explain them.
Measured before you start and again on the same instrument at the end, so the change is recorded rather than claimed.
A 30-minute diagnostic: 40 questions across the six domains. Not scored for the credential — it sets your baseline, shows which modules to spend time on, and is re-sat at 90 days.
Kinds of learning, where ML fits among other software, what it cannot do, and the vocabulary the rest of the programme uses.
Quality, labelling, bias, privacy, splits - and why a model can only ever be as honest as its data.
Training, overfitting, baselines, and why accuracy is the wrong number more often than you would think.
How an LLM produces text, prompting that works, hallucination, retrieval in one picture, and what 'the model is confident' really means.
Risk, fairness, intellectual property, confidentiality, human oversight - and your organisation's own AI policy, read closely.
Choosing a use case, reading a model's output critically, briefing and questioning the people who build models.
Forty questions, forty-five minutes, exam conditions. Drawn from the practice pool — nothing here appears in the final. Your report lists the lessons to revisit.
A second forty-question practice paper with different questions. Compare it with the first: the domains that moved and the ones that did not.
Sixty questions, seventy-five minutes, under exam conditions. Pass: 70% overall and at least 60% in every domain. One free retake after seven days.
Finishing this pathway issues a credential backed by the evidence you produced along the way. It is verifiable by link and is also issued as an Open Badge 2.0, so an employer can check it without taking your word for it.
Supervised, unsupervised and reinforcement learning in plain language, and how machine learning differs from software written as rules.
How data is collected, labelled and split, where bias enters, and what privacy means for the data a model is trained on.
Training, overfitting and baselines, and how to choose between accuracy and other measures when the cost of each kind of error differs.
How a large language model produces text, why it can sound confident and be wrong, prompting that works, and retrieval in one picture.
Risk, fairness, intellectual property, confidentiality and human oversight, taught at principle level alongside your own organisation’s AI policy.
Choosing a use case worth doing, reading an AI output critically before you rely on it, and asking useful questions of the people who build models.
Want the detail before you decide? Read the guide: AI and machine learning explained for working professionals.
| What is compared | A typical AI course | AI/ML Foundations at Bodhih |
|---|---|---|
| Where you start | Everyone starts at lesson one, whatever they already know. | A 40-question diagnostic across six domains sets your baseline and shows which modules need your time. |
| What is covered | Often either prompting tips for one tool or a mathematics-first syllabus that assumes you code. | Six no-code modules: what ML is, data, how models are judged, generative AI and LLMs, responsible use, and AI at work. |
| Practice before the test | Short quizzes after each lesson, if any. | Two forty-question mock exams with different questions. None of them appear in the final, and your report lists the lessons to revisit. |
| How you are assessed | A certificate of completion once the lessons are marked as finished. | A 60-question, 75-minute certification exam under exam conditions. Pass: 70% overall and at least 60% in every domain. |
| What you hold at the end | A PDF that says you attended. | A verifiable credential, also issued as an Open Badge 2.0, and a re-measurement at 90 days that shows whether the understanding held. |
| What comes next | A separate course, often repeating the same basics. | Level 2, Certified Applied AI/ML Practitioner, adds Python, labs and a reviewed project. Level 3 goes deep into LLM engineering. |
Straight answers on time, price, tools and the credential. If yours is not here, ask us and you will have a reply the same working day.
Yes. AI/ML Foundations from Bodhih is an AI ML certification for beginners that requires no coding at all. The course teaches how machine learning and generative AI work at a working level through six modules, then examines that understanding in a 60-question certification exam. You need to be comfortable reading English and using a web browser; you do not need Python, statistics or a technical degree.
The AI/ML Foundations course takes about 12 hours in total, and most people spread it over roughly 4 weeks alongside their job. The course is self-paced and online, so you can move faster or slower. The time covers a 30-minute diagnostic, six modules of one to two hours each, two 45-minute mock exams and the 75-minute certification exam.
The AI/ML Foundations course costs ₹9,999 plus GST for an individual, with 18% GST applied for Indian billing addresses. You pay online by UPI, card or netbanking, get access the moment payment is confirmed, and receive a GST invoice. The price includes the diagnostic, six modules, both mock exams, the certification exam with one free retake, and the credential. Team seats are ₹7,500 per seat for companies.
The AI/ML Foundations certification exam has sixty questions in seventy-five minutes and is sat online under exam conditions on AssessAll, Bodhih’s sister assessment platform. To pass you need 70% overall and at least 60% in every one of the six domains, so a strong score in one area cannot hide a gap in another. One free retake is available after seven days.
Yes. Passing the AI/ML Foundations certification exam earns a verifiable credential that is also issued as an Open Badge 2.0, so an employer can check that it is genuine instead of taking a PDF on trust. The credential is issued by Bodhih Training Solutions, a Bengaluru corporate training company operating since 2008. It certifies an examined foundation; it is not a university degree or a government licence.
The AI/ML Foundations course does not depend on any one AI tool. Level 1 has no labs, and every principle is taught tool-agnostically, so what you learn about prompting, hallucination and reading output critically applies whether you use Claude, ChatGPT, Microsoft Copilot or Gemini at work. You do not need a paid AI subscription to complete the modules or sit the exams.
Yes. AI/ML Foundations is designed as an AI course for working professionals, whether or not they write code. It was written for a mixed workforce: engineers and analysts alongside people in product, operations and management. The modules explain how models learn and how large language models work using plain language and workplace examples, and the exam tests understanding and judgement, not programming.
AI/ML Foundations includes a full module on responsible AI basics: risk, fairness, intellectual property, confidentiality and human oversight, and how to read your own organisation’s AI policy closely. Privacy and intellectual property are taught at principle level so you recognise when a question needs a specialist. The course is not legal, tax or professional advice.
After AI/ML Foundations Level 1 you can continue to Level 2, Certified Applied AI/ML Practitioner, and then Level 3, Certified AI/ML Specialist in LLM Engineering. Level 2 assumes you can read and modify Python and adds code-along modules, two labs and an applied project reviewed by a subject expert. Level 3 adds a capstone system and a viva. Level 1 supplies the vocabulary both build on.
Yes. AI/ML Foundations is sold to teams at ₹7,500 per seat for companies, and it works as AI literacy training for employees, giving technical and non-technical staff one examined baseline. Bodhih also runs in-house workshops, which have no public price. To discuss seats or a workshop, write to solutions@bodhih.com or call +91 99000 11601.
The best way to learn AI and machine learning from scratch is to pick a course that starts from what you already know, explains how models work and fail instead of only listing tool tips, covers responsible use, and tests you at the end. AI/ML Foundations from Bodhih meets those points with a diagnostic, six no-code modules, two mock exams and a certification exam with a pass mark in every domain.
AI/ML Foundations is ₹9,999 plus GST, about 12 hours, and open the moment your payment is confirmed. Enrol today and start with the diagnostic: thirty minutes that show you where you stand across all six domains. Then work through the modules, sit the mocks, and take the certification exam when your report says you are ready.
Starting records where you stand today. That baseline is what the re-measurement is compared against, and it is never rewritten afterwards.
Level 2: hands-on machine learning and LLM applications in Python, proved by two labs, a reviewed project and an exam.
Eight graded work products from your own accounts, a proctored exam and a credential anyone can verify.
For first-time and new people managers: run your team’s week with an AI assistant, and keep every decision about a person your own.