Every class is live, one-on-one and set to your hours.See all 243 courses →
AI - Artificial Intelligence AI & Machine Learning

AI - Artificial Intelligence

Artificial intelligence is reshaping every industry, and this course demystifies it with real, hands-on work. You'll learn the core concepts, work with Python and AI libraries, and build a small AI project of your own so it's not just theory. It's an exciting, future-focused entry into the field everyone's talking about.

Duration
1.5 Months
Format
Live 1-on-1
Level
Professional
Certificate
Osian NSDC
Overview

What this course is about

Explore Artificial Intelligence through a guided path that connects core concepts with tools people use at work. Live 1-on-1 online classes give you a personal teacher for Python practice, machine learning foundations and discussions about where AI is reliable or risky. You learn by building small experiments, asking questions about outputs and explaining the difference between automation, prediction and generation.

The syllabus begins with Python and data handling, then introduces machine learning foundations, neural network ideas and the role of training data. You also practise generative AI tools, prompt structure, context setting, iteration, output checking and responsible use. Activities may include text drafting, classification, summarisation or workflow design, while the teacher highlights privacy, bias, hallucination and human review. You test ideas rather than copy outputs.

Your portfolio can include a Python exercise, a simple model or neural network exploration, and a documented generative AI workflow with tested prompts. This broader AI path differs from the Machine Learning course, which concentrates on scikit-learn modelling and evaluation. Osian Academy provides a completion certificate.

By the end

What you’ll be able to do

  • Write practical Python code for basic AI and data tasks
  • Explain machine learning foundations, training data and model behaviour
  • Describe neural network components and their role in learning patterns
  • Use generative AI tools with structured prompts and useful context
  • Evaluate AI outputs for accuracy, bias, privacy and human review needs
  • Present an AI workflow with clear assumptions and responsible-use notes
Syllabus

How the course flows

  1. 1
    Python & Maths for AIWeek 1

    The Python, statistics and linear-algebra basics ML runs on.

  2. 2
    Working with DataWeek 2

    Loading, cleaning and exploring datasets that models can learn from.

  3. 3
    Core ML AlgorithmsWeek 3

    Regression, classification and clustering — how models actually learn.

  4. 4
    Model Training & EvaluationWeek 4

    Training, testing and measuring whether a model is any good.

  5. 5
    AI ProjectWeeks 5–6

    A complete build, from raw data to a working, evaluated model.

Toolkit

Tools you’ll use

  • Python
  • NumPy & pandas
  • scikit-learn
  • TensorFlow / Keras
  • Jupyter Notebook
Audience

Who this course is for

  • Students and graduates curious about practical Artificial Intelligence
  • Python beginners who want a guided AI learning path
  • Working professionals exploring AI-assisted workflows
  • Business owners seeking responsible uses of generative AI tools
  • Job seekers building an introductory AI portfolio
Where it leads

Roles this opens up

  • AI Project Trainee

    Support small AI experiments, document prompts and organise output checks under supervision.

  • Generative AI Workflow Assistant

    Design repeatable prompt-based workflows and review outputs against business requirements.

  • Junior Python and Data Associate

    Use Python foundations and data reasoning to assist with introductory AI tasks.

Questions

Before you enrol

No advanced background is required, though basic computer confidence and willingness to practise code are helpful. Python and the necessary machine learning foundations are introduced in context.

A laptop, browser and stable internet are recommended, along with a Python notebook environment for coding practice. Generative AI tools can change, so the teacher will explain the available tool access and safe account practices.

You can combine a Python exercise, a neural network or ML exploration and a generative AI workflow with tested prompts. The project includes output checking and notes about privacy, bias and human review. You test ideas rather than copy outputs.

You practise using AI for structured drafting, classification, summarisation and workflow support while checking accuracy. This can improve your contribution to business teams, but it does not promise a particular job or role.

The AI course covers a wider landscape including Python, ML foundations, neural networks, generative AI tools and prompt use. Machine Learning is more focused on supervised and unsupervised learning with scikit-learn and model evaluation.

You receive an Osian Academy course certificate on completion. The programme does not claim to provide official certification from OpenAI, Microsoft, Google or another AI tool provider.