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Job-Ready Track · From Tools to Job-Ready

Artificial Intelligence:
Level 2

5.0 (2,640 reviews)

A build-first, three-month programme that takes you from using AI tools well to building the things yourself: chatbots, AI-made websites, private AI running locally with Ollama, a JARVIS-style voice assistant and computer-vision apps. You finish with a public portfolio, live links, a tailored resume and mock-interview practice, ready to apply for AI, automation, data and web internships.

Created by Avanteia
5,180 Total Enrolled
02 Oct 2026 Last Updated
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Job-ready AI Level 2 course in Goa – building chatbots, AI websites, voice assistants and computer vision apps at Avanteia
3 Months Duration
Certificate On Completion
Level-02 Level
13 Modules Syllabus
3 Months Duration
120 Hours 36 Labs · 7 Projects
English Language

Overview

AI Level 2 is Avanteia's complete job-ready AI programme, and it is build-first by design. The opening modules teach you to get real work out of ChatGPT, Gemini and Claude and to automate repetitive tasks with a human approving every step. The middle modules add Python, pandas, SQL and dashboards, then move into the builds clients and employers ask for: AI running on your own laptop with Ollama, chatbots with RAG, websites made with AI, a JARVIS-style voice assistant and object detection with OpenCV and YOLO. The final modules turn your work into a public portfolio, a tailored resume and interview practice, ending with a team capstone and Demo Day. Classes run Monday to Friday, about 70% hands-on, with every Friday a Build Day — and five responsible-AI rules (verify, protect, consent, disclose, be honest) run through every module.

Prompt Engineering AI Automation Python pandas & SQL Ollama Chatbots & RAG AI Websites Voice Assistant Computer Vision Career Ready

Learning Outcome

Prompt reliably, automate repetitive work, write Python and SQL, run AI locally, build chatbots, websites, a voice assistant and computer-vision apps — and apply for internships with seven portfolio projects, live links and interview practice behind you.

Syllabus

Click any module to expand and view topics, hands-on labs and tools used.

  • What AI is, what generative AI is, and what neither one is
  • AI vs. Machine Learning vs. Deep Learning (vocabulary for interviews, no maths)
  • What a language model really does: predicting the next token
  • Tokens, context windows, and why long chats drift
  • Hallucination: why fluency is not accuracy
  • Where AI genuinely helps, and where a human stays accountable
  • The six-part prompt: role, task, context, format, tone, example
  • Few-shot prompting and chain-of-thought for multi-part tasks
  • Prompt hygiene: what must never be pasted into a public AI tool
Hands-on Lab
Compare ChatGPT, Gemini & Claude on One Brief Induce & Document an AI Hallucination Rebuild 5 Weak Prompts with the Six-Part Method Few-Shot Classifier & Chain-of-Thought Prompt Build a Personal Prompt Library
Tools Used
ChatGPT Gemini Claude
  • The outline-first writing workflow
  • Teaching an AI model your own voice from writing samples
  • The tells of AI writing, and how to remove the machine tone
  • Repurposing one asset into six platform formats
  • Ad copy frameworks (AIDA, PAS, FAB) and scroll-stopping hooks
  • Image prompts: subject, setting, style, light, mood
  • Video prompt formula: subject, action, scene, camera, style, audio
  • Design rules that rescue amateur work; copyright and AI disclosure
Hands-on Lab
Style Profile & Voice-Matched Article Anti-AI-Tone Editing Pass Repurpose One Piece into 5 Formats Mini Campaign for a Local Business Canva AI Visual & 7-Day Content Calendar 15-Second AI Video Reel with Voiceover
Tools Used
ChatGPT / Claude Canva AI Google Veo / Runway / Kling CapCut
  • Structure before slides: the six-beat story spine
  • Grounding: why an answer from a supplied source beats one from memory
  • Deep research mode, and why every citation still gets opened
  • The verification rule: what you check versus what you can accept
  • What is worth automating: repeated, rule-based, low-risk tasks
  • Anatomy of an automation: trigger, AI step, action
  • Keep it basic: one trigger, one AI step, one human approval
  • Human in the loop: approving before anything is sent
Hands-on Lab
Sourced Research & Fact Verification AI Presentation in Gamma & 2-Minute Delivery Document Q&A with Targeted Questions Email Triage & Structured Meeting Minutes Form → Sheet → AI Draft → Human Approval Automation Project A: AI Campaign Pack
Tools Used
Gemini Gamma Zapier / Make Google Forms Google Sheets
  • Terminal basics: folders, paths and daily commands (cd, ls/dir, mkdir, running a script)
  • Variables, data types, input and output
  • Conditionals, loops, lists and dictionaries
  • Functions: packaging steps so they can be reused
  • Reading a traceback: the four errors beginners actually hit
  • File modes, and why the wrong one silently erases your data
  • CSV for tables, JSON for structure, and when to use each
  • What an API is: requests, JSON responses, status codes and timeouts
  • Modules, pip and virtual environments
  • Automation scripts: dry runs first, real changes second
  • Git as a save-point system: commit, branch, push, pull
Hands-on Lab
Install Python & VS Code Locally Command-Line Assistant Run from the Terminal JSON To-Do List with File Memory Call a Public API with requests Folder Sorter, Bulk Renamer & CSV Merger Scripts First GitHub Repository with README
Tools Used
Python VS Code Terminal requests pathlib & shutil Git & GitHub
  • The data pipeline: collect, clean, explore, explain
  • pandas DataFrames and Series
  • The four data problems: missing values, duplicates, wrong types, inconsistent text
  • Filtering, sorting, groupby, pivot tables and merge
  • Relational databases: tables, keys and relationships
  • SQL: SELECT, WHERE, GROUP BY, JOIN, ORDER BY
  • Exploratory analysis: distributions, outliers, correlation is not causation
  • Using an AI assistant as a pair programmer — and reading what it writes
Hands-on Lab
Clean a Messy Dataset with a Cleaning Log SQL Sprint: 8 Business Questions Catch an AI Assistant's SQL Mistake Five Data Insights in a One-Page Summary
Tools Used
pandas Jupyter / VS Code Notebooks SQLite DB Browser for SQLite
  • Choosing the chart: comparison, trend, share, distribution, relationship
  • Misleading charts: truncated axes, cherry-picked ranges, 3D effects
  • Data storytelling: headline, evidence, so-what
  • Dashboard thinking: KPIs, filters, one question per view
  • Why a web app is shareable and a notebook is not
  • Streamlit widgets, metrics, file upload and free public deployment
Hands-on Lab
Find & Fix a Misleading Chart Project B: Business Insight Dashboard Deploy a Streamlit App to a Public Link
Tools Used
Matplotlib Streamlit Streamlit Community Cloud GitHub
  • Cloud AI vs. local AI: privacy, cost, speed, quality, internet dependence
  • Open models: Llama, Gemma, Qwen, Phi and others
  • Model size in plain words: parameters, RAM, and why small models are fast but simpler
  • Quantisation: shrinking a model to fit your laptop
  • Where local AI makes sense: private documents, offline use, zero API bills
  • Where cloud AI still wins: the hardest reasoning, long inputs, weak hardware
  • Local does not mean accurate: verification still applies
  • Talking to Ollama from Python and creating a named assistant with a Modelfile
Hands-on Lab
Install Ollama & Run Two Local Models Local vs. Cloud Comparison Sheet Prove the Model Works Offline Offline Study Buddy in Python Custom Assistant with a Modelfile
Tools Used
Ollama Llama / Gemma / Qwen / Phi Python Open WebUI / LM Studio
  • What a chatbot is: instructions, conversation memory, knowledge and tone
  • The system prompt as the chatbot's job description
  • Scope and guardrails: staying on topic, saying "I don't know", handing over to a human
  • Choosing the brain: cloud API or a local model through Ollama
  • How code calls a model: messages, temperature, max tokens
  • API keys are secrets: .env files and .gitignore
  • RAG in plain words: find the right passage, answer from it, cite the page
  • Prompt injection: how users try to override a bot, and how to test for it
  • Testing with normal, tricky, out-of-scope and injection questions
Hands-on Lab
No-Code Business Assistant with a Knowledge File Code a Chatbot with Memory via an LLM API Ask-My-Document Bot (RAG) with Page Citations Prompt-Injection Test Sheet Project C: Business Chatbot on a Public Link
Tools Used
Custom GPT / Gemini Gem / Claude Project OpenAI / Gemini / Claude API Ollama Streamlit Chat .env & .gitignore
  • How a website works: HTML for structure, CSS for looks, JavaScript for behaviour
  • The website brief: audience, pages, tone, colours, one clear call to action
  • The AI website workflow: brief, structure, build, refine, test, deploy
  • Reading AI-generated code: if you cannot run it and explain it, do not ship it
  • Mobile-first and responsive design; type, spacing, contrast and white space
  • Local-business must-haves: WhatsApp click-to-chat, map, contact form, phone number
  • SEO basics: page titles, meta descriptions, headings, alt text, speed
  • Hosting, domains and free deployment; image licensing and copyright
  • Google Antigravity as a supervised AI coding assistant — review every change
Hands-on Lab
Hand-Edit an AI-Generated Page with DevTools Write a Structured Website Brief AI Website Builder vs. Antigravity Comparison Lighthouse Speed, Accessibility & SEO Fixes Project D: AI-built Business Website on a Live Link
Tools Used
Lovable / Bolt / v0 Google Antigravity VS Code Chrome DevTools Lighthouse Google Forms / Formspree GitHub Pages / Netlify / Vercel
  • How text-to-speech works; offline versus online voices
  • How speech recognition works, and why it mishears
  • Ambient noise, energy thresholds and timeouts so a program never hangs
  • The three recognition errors, and handling each explicitly
  • Keyword matching over exact matching; wake words and conversation flow
  • Assistant design: input, intent, action, output; routing commands to skills
  • The brain: sending open questions to a local model (Ollama) or a cloud API
  • Graceful failure, personality, and the privacy of an always-listening device
Hands-on Lab
Give Python a Voice (incl. Hindi & Marathi) Listen–Understand–Respond Loop Wire 5 Spoken Commands to 5 Actions Real-Condition Accuracy Testing Project E: JARVIS-style Voice Assistant with Wake Word
Tools Used
pyttsx3 gTTS SpeechRecognition PyAudio Ollama
  • An image as a grid of numbers; channels, and why OpenCV uses BGR
  • Greyscale, blur, edge detection and thresholding, and what each is for
  • Video as a stream of frames processed like photographs
  • Detection versus recognition, and why the difference matters legally
  • Face detection with Haar cascades; tuning false positives and missed detections
  • Object detection with a pretrained YOLO model: box, label, confidence
  • The confidence threshold trade-off: more detections or fewer mistakes
  • What breaks detection: light, angle, occlusion, distance, motion
  • Consent-first labs: never used to monitor, track, score or screen people
Hands-on Lab
Image Processing & Canny Edge Tuning Live Webcam Processing Face-Aware App (Counter, Photo Booth or Face Blur) Count Objects & Chart Detections with pandas Project F: Object Detector with a Failure Record
Tools Used
OpenCV Haar Cascades YOLO (Ultralytics) pandas Streamlit
  • What recruiters scan for in the first 30 seconds
  • Role map: AI content & prompt specialist, data analysis intern, AI chatbot developer intern, AI automation associate, junior web builder, AI project assistant (vision)
  • Resume: one page, impact verbs, numbers, ATS keywords — every line traced to something you built
  • GitHub as a portfolio: pinned repos, READMEs, commit history
  • LinkedIn: headline, About, Featured links
  • Explaining a project in two minutes: problem, approach, result, limits
  • Technical rounds: Python, SQL, prompting, "walk me through your project"
  • HR rounds, stipend discussion, and finding internships and freelance work honestly
Hands-on Lab
Resume Tailored to Two Real Postings LinkedIn & GitHub Portfolio Polish 50-Question Interview Bank Technical & HR Mock Interviews Internship Sprint: 10 Tailored Applications
Tools Used
GitHub LinkedIn STAR Story Bank Application Tracker
  • Problem first: who has it, and how will we know it is solved
  • Scoping: minimum viable version versus nice-to-have
  • Sprint planning with GitHub Projects or Trello; daily stand-ups
  • Code review: reading a classmate's pull request kindly and usefully
  • Documenting: README, limitations and a one-page case study
  • Demo craft: problem, build, result, limitation in three minutes
  • Capstone options:
    • AI customer-support chatbot with a website for a local business
    • Private offline study assistant on Ollama
    • JARVIS-style assistant with a vision skill
    • Object-counting app with a dashboard of its logs
    • A project approved by the trainer
Hands-on Lab
Sprint Board & GitHub Issues Peer-Reviewed Pull Request Deploy & Test on a Fresh Machine Two-Minute Demo Video Portfolio Case Study Capstone Demo Day
Tools Used
GitHub Issues & Projects Pull Requests Antigravity / AI Coding Assistant Trello

Free Internship Opportunity Included

What You Will Learn

AI Tools & Automation

Prompt ChatGPT, Gemini and Claude reliably, create content and video, and automate work with human approval.

Python, SQL & Dashboards

Write Python, clean data with pandas, query with SQL and deploy a live Streamlit dashboard.

Local AI & Chatbots

Run private AI with Ollama and build, test and deploy chatbots with memory, RAG and injection tests.

AI-Built Websites

Go from a brief to a live, mobile-friendly business website with AI, and fix what the AI gets wrong.

Voice & Computer Vision

Build a JARVIS-style voice assistant and detect faces and objects with OpenCV and YOLO.

Portfolio & Interviews

Launch with seven projects, GitHub repos, a tailored resume, LinkedIn and two scored mock interviews.

What Our Students Say

"

The Level 2 AI course took my skills to a professional level. The CNN and transformer labs were exactly what I needed to land my first machine learning engineer role. Avanteia's hands-on approach is unmatched.

Vikram Patil Machine Learning Engineer, Mumbai
"

I completed AI Level 1 first and immediately enrolled in Level 2. The NLP and computer vision modules were eye-opening. The 3-month duration is perfect for working professionals.

Sneha Kadam AI Analyst, Pune
"

The deployment modules with FastAPI, Docker, and Kubernetes are incredibly relevant. I used the skills from this course to ship my company's first production AI model. Highly recommended for anyone serious about AI.

Rahul Menon AI Engineer, Bangalore

Frequently Asked Questions

None. No coding, engineering or mathematics background is required. Level 2 is the complete job-ready programme: it starts with AI tools and prompt engineering, teaches Python from scratch, and builds up to chatbots, websites, a voice assistant and computer vision.

A laptop with 8 GB RAM or more, a working microphone and a webcam. Local AI runs best with 16 GB RAM, but small models run on 8 GB and every local-AI lab has a cloud fallback. Every lab runs on your own machine and accounts.

The course is 120 contact hours over 12 weeks (3 months): Monday to Friday, 2 hours a day. Monday to Thursday is learn and lab, and Friday is Build Day. Classroom training is available at Avanteia centers in Mapusa, Panjim, Margao, Sanquelim, Pernem and Mysore in India, plus Las Vegas in the USA. An online live mode is also available.

Yes. On successful completion of all 13 modules, the hands-on labs and the capstone project, you receive an industry-recognized Avanteia certificate that can be verified online through our verification portal.

You finish with seven portfolio projects, six or more public GitHub repositories, a live website, chatbot and dashboard, a tailored resume and two scored mock interviews. You will be ready to apply for internship and entry-level roles such as AI content and prompt specialist, AI-enabled digital marketing executive, data analysis intern, AI chatbot developer intern, junior web builder, AI automation associate or AI project assistant (vision), and you can join our internship program. Hiring outcomes depend on effort, the market and the employer.

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