Explain the difference between supervised and unsupervised machine learning using simple examples.
Live remote learning · Machine learning
Machine Learning with Python
A practical introduction to supervised and unsupervised machine learning using scikit-learn. Learn how to prepare data, train simple models, evaluate results, and understand what the model is doing.
Supervised
Classification and regression
Unsupervised
Clustering and patterns
scikit-learn
Simple ML workflows
Metrics
Evaluate model performance
Why this course
Learn machine learning without jumping into advanced theory
Machine learning becomes easier when learners first understand the basic workflow: load data, clean features, split the dataset, train a model, test it, and interpret the result. This course focuses on that practical foundation before moving into complex methods.
The course uses simple supervised and unsupervised models from scikit-learn. The goal is to help learners build confidence with real notebooks, clear examples, and models that are easy to explain.
Learning outcomes
At the end of this training, learners will be able to
Prepare datasets using basic feature selection, missing-value handling, scaling, and train-test splitting.
Train simple classification models such as LogisticRegression, KNeighborsClassifier, and DecisionTreeClassifier.
Build basic regression models such as LinearRegression and DecisionTreeRegressor.
Use unsupervised models such as KMeans and simple dimensionality reduction with PCA.
Evaluate models using accuracy, confusion matrix, precision, recall, F1-score, MAE, RMSE, and R².
Detailed program
A practical four-module machine learning path
The course uses short explanations, guided notebooks, and hands-on exercises with scikit-learn.
01
Machine learning workflow
What machine learning is, supervised vs unsupervised learning, features and labels, training and testing data, overfitting, underfitting, and the basic scikit-learn workflow.
02
Data preparation for ML
Loading datasets, selecting features, handling missing values, encoding simple categories,
scaling numeric features, using train_test_split, and building clean input data for models.
03
Supervised learning: classification and regression
Simple classification with Logistic Regression, K-Nearest Neighbors, Decision Tree, and Random Forest. Basic regression with Linear Regression and Decision Tree Regressor. Evaluation using clear metrics and visual checks.
04
Unsupervised learning and model comparison
Clustering with K-Means, understanding clusters, using PCA for simple visualization, comparing models, reading results carefully, and presenting findings in a clear and honest way.
Required background
Basic Python and data handling are enough to start
This course does not require advanced mathematics or deep learning. Learners should know basic Python, variables, functions, loops, lists, and simple Pandas DataFrame operations.
Discuss your level
Target learners
Students, beginners in data science, researchers, and professionals starting machine learning.
Prerequisites
Basic Python, simple data analysis, and comfort with Jupyter notebooks.
Recommended prior courses
Python Programming and Python & Data Science (Numpy, Pandas, Matplotlib).
Teaching method
Simple models, clear notebooks, practical exercises
Each topic is introduced with a short explanation and then applied directly in Python. Learners train models, inspect outputs, calculate metrics, and discuss what the results actually mean.
The course avoids unnecessary complexity. The focus is on understanding the standard machine learning workflow and becoming comfortable with scikit-learn for practical beginner-level projects.
FAQ
Common questions
Is this course suitable for beginners?
Yes. It is suitable for learners who know basic Python and want to start machine learning in a practical way.
Does this course include deep learning?
No. This level focuses only on simple supervised and unsupervised machine learning models using scikit-learn.
Which models are covered?
The course covers beginner-friendly models such as Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Linear Regression, K-Means, and PCA for simple visualization.
Will learners build small projects?
Yes. Learners complete guided notebook exercises and small practical tasks for classification, regression, clustering, and model evaluation.
Start learning
Build your first machine learning workflow
Share your current Python level, learning goals, and preferred schedule. We will suggest the right course path for starting machine learning with confidence.