Name: Introduction to Machine Learning
Price: 13000 PKR
Availability: InStock

About this course

Train, evaluate and honestly interpret your first models - the foundation for every advanced AI track.

This is a 6-week short course - enough depth to build real projects and a portfolio piece, without a long commitment. It runs online in the August 2026 cohort (starting 1 August 2026) and is taught the EchoLens way: you learn by doing real, gradeable work rather than just watching lectures.

What's included

Live, instructor-led online sessions across 6 weeks (24 hours total).
Hands-on coding quests you solve inside the EchoLens browser compiler - nothing to install.
Gems, stages and a leaderboard that keep you moving instead of grade anxiety.
A verified certificate with a scannable QR code, ready to share on LinkedIn, when you finish.
The first week is open free so you can try the course before you pay.

What you will learn

The ML workflow and data preparationRegression and its evaluation metricsClassification and the confusion matrixTrees, KNN, and Naive BayesFeature engineering and encodingCross validation and hyperparameter tuningEnsembles, clustering, and PCAHonest evaluation and avoiding leakage

Course learning outcomes

By the end of this course, you will be able to:

CLO 1. Frame a prediction problem and prepare data with feature engineering and encoding for modelling.
CLO 2. Train and tune regression, classification and ensemble models using cross-validation.
CLO 3. Evaluate a model honestly, avoiding overfitting and leakage, and defend the result in plain English.

Course outline - level by level

12 leveles, each with hands-on quests you clear in the portal.

Level 1. What machine learning is · Data for ML - Supervised versus unsupervised, the ML workflow and where ML fits, then features, labels, the train and test split, and why held-out data matters.
Level 2. scikit-learn refresher · Your first model - The estimator API, loading a dataset, and the consistent fit, predict, evaluate workflow - a first baseline model and how to read its result.
Level 3. Linear regression · Regression metrics - Fitting a line, reading coefficients and predicting a number, then judging models with MAE, MSE and R squared and comparing them fairly.
Level 4. Overfitting and underfitting · Regularisation - Bias and variance and the signs of each, then why regularisation helps and how ridge and lasso build simpler, sturdier models.
Level 5. Logistic regression · Classification metrics - Predicting a class with probabilities and thresholds, then accuracy and its traps, precision, recall, F1 and the confusion matrix.
Level 6. Decision trees · KNN and Naive Bayes - How trees split and how to read them, distance-based prediction with KNN, the probabilistic Naive Bayes classifier, and when each suits.
Level 7. Feature engineering · Encoding categories - Creating useful features and scaling numeric data, then one-hot and label encoding and handling many categories for clean model inputs.
Level 8. Cross-validation · Tuning models - Beyond a single split to k-fold scores you can trust, then hyperparameters and grid and random search - better without overfitting.
Level 9. Ensembles · Clustering - Random forests and the gradient boosting idea and why ensembles win, then k-means, choosing k, and finding groups without labels.
Level 10. Dimensionality reduction · Interpreting models - The curse of dimensions and the PCA idea for simplifying data, then feature importance and explaining a prediction for trust and transparency.
Level 11. A full pipeline · Honest evaluation - Assembling prep, train and evaluate into one clean workflow, then data leakage, realistic test setups, and not fooling yourself.
Level 12. Saving and using a model · Framing an ML solution - Persisting a model and predicting on new data, then deciding whether ML is the right tool, scoping the problem, and delivering a model that helps.

How you submit: Coding quests in the built-in compiler with an AI copilot beside the editor - exactly the Cursor/Copilot workflow the course teaches.

Your production end project

End project - your production build

A Trained, Evaluated Prediction Model

A model that works on data it has never seen, with the evaluation to prove it.

Take one real dataset and build a complete machine learning solution end to end: frame the problem, prepare the data, train and tune a model, and evaluate it honestly. The emphasis is on a result you can defend, not a number you can brag about.

✓A clearly framed prediction problem and why ML suits it
✓A cleaned dataset with engineered and encoded features
✓A baseline model to beat, stated up front
✓At least two algorithms trained and compared fairly
✓Cross validation rather than a single lucky split
✓Hyperparameter tuning with the improvement documented
✓The right metrics for the problem, with the confusion matrix or error analysis discussed
✓A written check for data leakage and overfitting
✓The saved model making a prediction on new unseen data

Shipped when: The model runs on fresh data, beats the stated baseline, the scores come from cross validation, and you can explain in plain English what it predicts, how well, and where it fails.

Who it's for

Introduction to Machine Learning suits learners at a beginner to intermediate level who want a practical, project-based route into Introduction to Machine Learning. You need only a browser and an internet connection - all coding runs inside the EchoLens compiler, so there is nothing to set up.

Certificate

Finish every stage and EchoLens issues a verified certificate carrying a QR code anyone can scan to confirm it on our site. You can add it to your CV or share it to LinkedIn in one click.