AI Engineering Programme
AI Course with Practice Labs and Placement Support
AI Engineer
Overview
The AI course is the ai-engineering track at Career Catalyst Institute. The track description is statistics, machine learning, the Python data stack, prompting, and RAG systems for applied AI roles. The default role is AI Engineer. The campus landing page lists the same topics: ML foundations, NumPy and Pandas, scikit-learn basics, LLMs and prompting, a RAG overview, evaluation basics, API integration, and Git for AI work. Outcomes listed there are the AI track curriculum, category assessments, and roles matched to AI profiles.
After you create a student account, this programme sits inside the same CCI Academy login as the other three tracks. The campus already includes a student dashboard, learner profile, assessment hub, learning roadmap, course lessons, practice labs, a resume builder, a certificates vault, and a placement desk. Faculty use the admin campus for students, curriculum, assessments, placements, certificates, and mailbox. AI Mentor, Interview Prep, and Community Hub are routed in the product and marked as coming next; they are not live lesson tracks.
The database seed for this track publishes Module 0: AI and Machine Learning Foundations, described as an introduction to machine learning, supervised versus unsupervised learning, and basic neural networks. The repair script titles the same slug Module 0: Introduction to Machine Learning, with the description “core ML ideas that start the AI and ML engineering track.” The first lesson in the seed is Introduction to Machine Learning and is marked Beginner, with a quiz. The assessment hub already scores a wider set than that single module: AI and ML basics, NumPy and Pandas, LLMs and prompt engineering, RAG and vector databases, AI integration workflows, and AI logic communication.
Syllabus and modules
Published lesson content found for this track is Module 0 and its introduction lesson. The AI and ML assessment describes supervised versus unsupervised learning, basic classifiers, loss functions, and overfitting. Sample questions in the roadmap bank ask what supervised learning uses, what overfitting means, and what inference means. The NumPy and Pandas assessment describes DataFrames, arrays, filtering, aggregations, and matrix multiplication. Sample questions define a DataFrame as a two-dimensional labeled table, a NumPy array as a homogeneous n-dimensional array, and groupby as aggregating rows by key columns.
The LLM assessment describes system prompts, context windows, token limits, and zero-shot or few-shot prompting. Sample questions cover few-shot prompting, temperature as randomness of generation, and a prompt that includes role, task, constraints, and format. The RAG assessment describes embeddings, cosine similarity, chunking, and vector search. Sample questions cover retrieval plus generation, embeddings for semantic search, and chunking as a change to what context is retrieved. The integration assessment describes deploying LLM endpoints, guardrails, and vector-store indexers. Sample questions cover guardrails, an agent that plans and may call tools or APIs, and human review of critical AI actions.
The campus page summarises the module arc as math and ML foundations, the data stack, LLMs and RAG, and shipping AI features. TODO: lesson titles for NumPy, Pandas, scikit-learn, prompting, and RAG beyond Module 0 are not in the AI seed script. Those names come from the track description and the assessment categories, not from additional seeded modules. The campus page also says Web, Testing, and AI labs are published as faculty open those curricula. This page does not list AI lab slugs, because the lab bridge in the frontend maps Python labs only.
Tools covered
Named in the track copy and assessments: machine-learning ideas including supervised and unsupervised learning, loss, and overfitting; NumPy and Pandas; scikit-learn as named on the landing page; prompts, tokens, and temperature; embeddings, chunking, and vector search; guardrails and tool-calling agents; Git as named on the landing page for AI work. TODO: library versions, model providers, vector-database products, and whether students receive API keys are not published in this LMS.
Duration, fees, and who can join
TODO: full course duration and total study hours are not published in this LMS. The seeded introduction lesson stores a duration of 20 minutes. That is one lesson field, not the course length. TODO: course fee is not published in this LMS. TODO: batch timing and class schedule are not published in this LMS.
The introduction lesson is marked Beginner. The Python track in the same academy covers the programming used by the data-stack assessments. TODO: a formal eligibility rule, including whether Python is required first, is not published in this LMS.
Career outcomes
The role is AI Engineer. The assessment report and roadmap are the same campus mechanism as the other tracks: category scores, a roadmap assessment limited to two attempts per day, and study order based on weaker categories. Category quizzes allow three attempts per day. Certificates, the resume PDF, and the placement desk are available on the student account. TODO: placement rate, salary figures, and hiring-company names are not published in this LMS.
Placement support
AI students use the same placement desk as Testing, Web, and Python: live roles, application status, notes, certificates, and an internship offer-letter request verified by faculty. Write to placements@cci.in.net for employers and info@cci.in.net for admissions. TODO: campus address is not published in this LMS. TODO: phone number is not published in this LMS.
Modules in this LMS
1. Module 0: AI & Machine Learning Foundations
Introduction to machine learning, supervised versus unsupervised learning, and basic neural networks. The repair script titles this slug Introduction to Machine Learning.
Tools named in the product
- ML ideas in the AI & ML assessment: supervised learning, overfitting, inference
- NumPy and Pandas as named in the data-manipulation assessment
- Prompt structure, few-shot examples, and temperature in the LLM assessment
- Embeddings, chunking, and retrieval in the RAG assessment
- Guardrails and tool-calling agents in the integration assessment
Items the institute still needs to fill
- TODO: course fee is not published in this LMS.
- TODO: full course duration and total study hours are not published in this LMS.
- TODO: batch timing and class schedule are not published in this LMS.
- TODO: campus address is not published in this LMS.
- TODO: phone number is not published in this LMS.
FAQ
The seed script publishes Module 0. NumPy, Pandas, prompting, RAG, and integration are assessment categories and landing-page topics. Extra lesson modules for those topics are not in the AI seed script.