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Machine Learning with Python

Build, evaluate, and tune machine learning models in Python with pandas and scikit-learn, moving from raw data to a model you can actually trust.

Material Overview
Machine Learning with Python

Learn practical machine learning with Python. You will prepare data with pandas, handle missing values and categorical features, and train regression, classification, and clustering models with scikit-learn. This E-Learning Material covers train and test splits, cross validation, the metrics that matter for each task, overfitting, feature importance, and saving a trained model so it can be reused. Basic Python is the only prerequisite.

Curriculum

Learning Path

Each level is enrolled separately. Work through them in order, or start at the level that matches your experience.

  1. Level 01

    Beginner

    Purpose

    Get from a raw dataset to your first working model using Python tools you will keep using.

    Outcome

    You can train a simple model on a real dataset and explain what it predicts.

    What You'll Learn
    • Load, inspect, and clean data with pandas and understand its shape
    • Handle missing values, categories, and simple feature scaling
    • Train a first model with scikit learn and read its basic accuracy

    249 CREDS

  2. Level 02

    Intermediate

    Purpose

    Learn to evaluate models properly, because a high training score usually means you are being fooled.

    Outcome

    You can tell whether a model will hold up on new data before you rely on it.

    What You'll Learn
    • Split data correctly and use cross validation to get a stable estimate
    • Choose metrics that suit the task, including precision, recall, and error measures
    • Diagnose overfitting and underfitting from learning curves

    499 CREDS

  3. Level 03

    Advanced

    Purpose

    Improve results with better features, stronger models, and tuning that is systematic rather than random.

    Outcome

    You can lift model performance measurably and explain which change caused the gain.

    What You'll Learn
    • Engineer features from dates, text, and categories that carry real signal
    • Train ensemble models such as random forests and gradient boosting
    • Tune hyperparameters with search methods and keep the process reproducible

    797 CREDS

  4. Level 04

    Expert

    Purpose

    Take a model out of the notebook and into a pipeline that other people and systems can depend on.

    Outcome

    You can deliver a model as a maintainable component rather than a one time experiment.

    What You'll Learn
    • Build reproducible pipelines that carry preprocessing along with the model
    • Serialise, version, and serve a model behind a simple interface
    • Monitor input drift and performance decay and plan retraining

    1,086 CREDS