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Machine Learning (ML) AI & Machine Learning

Machine Learning (ML)

Machine learning is how computers learn from data, and it's one of the most valuable skills in tech. This course covers the essential algorithms — regression, classification, clustering — using Python's ML libraries, and takes you through training and evaluating real models. Practical and focused, it gets you genuinely building ML.

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

What this course is about

Learn how machine learning models find patterns in data and how to judge whether those patterns are useful. In live 1-on-1 online classes, your teacher explains the workflow from problem definition and data preparation to training, evaluation and communication. The focus is practical: you write and inspect code, compare approaches and understand why a model may fail instead of treating a library call as magic.

The course introduces supervised learning for prediction and classification, unsupervised learning for clustering and pattern discovery, feature preparation, train and test splits, cross-validation and model evaluation. You work with Python and scikit-learn, exploring suitable metrics, baseline models, preprocessing pipelines, overfitting, underfitting and interpretation of results. Examples are chosen to make assumptions and limitations visible. You also practise keeping an experiment record.

A guided project takes a realistic dataset from exploration to a documented model and evaluation summary. This is a focused ML path for learners who want predictive modelling foundations; the AI course adds broader Python, neural networks, generative AI tools and prompt use. Osian Academy provides a completion certificate.

By the end

What you’ll be able to do

  • Prepare features, labels and datasets for machine learning experiments
  • Train supervised regression and classification models with scikit-learn
  • Apply unsupervised clustering to discover groups and patterns
  • Evaluate models with suitable metrics, validation and baseline comparisons
  • Recognise overfitting, leakage, bias and limitations in model results
  • Document a machine learning project with clear technical reasoning
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

  • Python learners ready to study predictive modelling
  • Data analysts moving towards machine learning projects
  • Engineering and science graduates exploring applied ML
  • Working professionals who want a structured ML foundation
  • Job seekers building a scikit-learn project portfolio
Where it leads

Roles this opens up

  • Machine Learning Trainee

    Prepare datasets, run baseline experiments and record model evaluation results under guidance.

  • Junior Data Scientist

    Support exploratory analysis, feature preparation and supervised or unsupervised modelling tasks.

  • Python Data Analyst

    Use Python and scikit-learn to investigate patterns and communicate evidence from data.

Questions

Before you enrol

Basic Python, spreadsheets and comfort with school-level mathematics are useful, but the teacher revisits the concepts needed for the exercises. You should be ready to read code and reason about data rather than only watch demonstrations.

A laptop with Python, a notebook environment and common packages such as pandas and scikit-learn is recommended. The teacher can guide setup, and cloud notebooks may be used when local installation is inconvenient.

You will take a dataset through exploration, preprocessing, model training, validation and evaluation, then document the result. The project also requires discussing limitations and why a chosen metric fits the task.

It gives you practice with a repeatable modelling workflow, experiment notes and evaluation language that can be discussed in interviews. Placement support includes resume help, mock interviews and referrals, not a guaranteed job.

Machine Learning focuses deeply on supervised and unsupervised learning, scikit-learn workflows and model evaluation. The AI course is broader, adding Python and ML foundations, neural networks, generative AI tools and prompt use.

It builds applied foundations and a guided project, not a claim of production engineering expertise. Deployment, large-scale infrastructure and advanced research require further study and workplace practice.