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+91 93074 02403 info@avanteia.com

Artificial Intelligence Level 1

Artificial Intelligence Track

Artificial Intelligence:
Level 1

4.9 (1,850 reviews)

Go from using AI tools well to building with AI yourself. Master prompting, AI content and basic automation, then learn Python, pandas and SQL, build a live Streamlit dashboard, run private AI on your own laptop with Ollama, and create, test and deploy a real business chatbot.

Created by Avanteia
8,420 Total Enrolled
02 Oct 2026 Last Updated
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AI Level 1 course in Goa – building chatbots, dashboards and local AI with Python at Avanteia Mapusa
2 Months Duration
Certificate On Completion
Level-01 Level
8 Modules Syllabus
2 Months Duration
68 Hours 22 Labs · 3 Projects
English Language
Introduction

Overview

AI Level 1 is a build-first course that needs no prior coding experience. You start by getting real work out of ChatGPT, Gemini and Claude — prompting, content, video and a simple no-code automation with human approval. You then learn Python, the terminal, APIs and Git, clean and query data with pandas and SQL, and publish a live Streamlit dashboard. Next you run private AI on your own laptop with Ollama and use it, or a cloud API, as the brain for chatbots with memory, document answers (RAG) and prompt-injection tests. Around 70% of class time is hands-on, every lab runs on your own machine, and you finish with three portfolio projects on public links.

Prompt Engineering AI Automation Python pandas SQL Streamlit Ollama Chatbots GitHub
Goal

Learning Outcome

Prompt reliably, automate repetitive work, write Python and SQL, turn messy data into a live dashboard, run AI locally with Ollama, and build, test and deploy a chatbot — with an AI Campaign Pack, a Business Insight Dashboard and a Business Chatbot to show employers.

Curriculum

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
Results

What You Will Learn

AI Tools & Automation

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

Python, SQL & Data

Write Python, call APIs, clean data with pandas, answer business questions with SQL and publish to GitHub.

Dashboards & Local AI

Deploy a live Streamlit dashboard and run private AI models offline on your own laptop with Ollama.

Chatbots & RAG

Build, test against prompt injection and deploy a business chatbot that answers from your own documents.

Testimonials

What Our Students Say

"

After completing the AI Beginner course, Level 1 took my skills to a professional level. The supervised learning labs and capstone churn prediction project are exactly what I needed to land my first data analyst role. Avanteia's hands-on approach is unmatched.

Vikram Parsekar Data Analyst, Mapusa
"

I completed the AI Beginner course first and immediately enrolled in Level 1. The NLP and computer vision modules were eye-opening, and deploying my own model with Flask was a huge confidence boost. The 2-month duration is perfect for working professionals.

Sneha Kulkarni ML Engineer, Panjim
"

The unsupervised learning and sentiment analysis labs are incredibly relevant. I used the skills from this course to build a recommendation engine for my company's e-commerce platform. Highly recommended for anyone serious about AI.

Rahul Naik Software Engineer, Margao
FAQ

Frequently Asked Questions

None. No coding, engineering or mathematics background is required. Level 1 starts with AI tools and prompt engineering, teaches Python from scratch, and then moves into data, local AI and chatbots.

A laptop with 8 GB RAM or more. Local AI with Ollama runs best with 16 GB RAM, but small models run on 8 GB, and every local-AI lab has a cloud fallback for weaker laptops.

The course has 68 contact hours across 8 modules and runs for about 2 months, with flexible batch timings at all centers — Mapusa, Panjim, Margao, Sanquelim, Pernem, Mysore and Las Vegas — plus an online mode.

Yes. You receive an industry-recognized certificate on successful completion, verifiable through our online verification portal.

You will have three portfolio projects: an AI Campaign Pack, a live Streamlit Business Insight Dashboard and a deployed Business Chatbot. You can progress to Artificial Intelligence Level 2 for AI-built websites, a JARVIS-style voice assistant, computer vision and career preparation, or apply for internships such as AI content and prompt specialist, data analysis intern or AI chatbot developer intern. Outcomes depend on effort, the market and the employer.

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