Hands-on Machine Learning with Scikit-learn

It is built for engineering and data science students, freshers who have finished a Python or pandas course and want to move on to machine learning, and analysts who work with DataFrames every day but have never fitted a model. You should be able to write Python functions and load, filter and group a DataFrame. The course does not teach Python or pandas basics; if you need them first, Data Analytics with Python covers NumPy and pandas. No machine learning background is assumed.

All code uses the current scikit-learn 1.x API with pandas and NumPy: the estimator methods fit, predict, predict_proba, transform and score, Pipeline and ColumnTransformer, set_output(transform="pandas") and HistGradientBoostingClassifier. Older habits, such as fitting a scaler before the split or running LabelEncoder on input features, appear only as mistakes to recognise. Deep learning, neural networks and deployment servers are outside the course, which stays on classical machine learning for tabular data.

Every lesson is a few short reading cards built around a small piece of scikit-learn code, with a diagram where one helps and audio narration you can listen to instead of reading. A short multiple-choice quiz follows, with an explanation for every answer. The questions ask about decisions rather than definitions: what a line returns, which split leaks information, whether StratifiedKFold or plain KFold suits a dataset, or which metric matches the cost of a mistake. Lessons are short enough to finish in one sitting, and the course map shows how each one fits into its topic. The last section puts the pieces together in practical scenarios: reviewing a churn model, segmenting customers, screening loans for default risk and building a sales forecasting baseline.

Many weak or misleading models come from workflow mistakes rather than the choice of algorithm. Each of these gets its own lesson:

• Fitting a StandardScaler on the full dataset before train_test_split, so test data leaks into training
• Target leakage, where a feature is only known after the outcome you are predicting
• Shuffling time-ordered data into a random split
• Running LabelEncoder on input features instead of one-hot or ordinal encoding
• Judging a classifier on imbalanced data by accuracy alone
• Reading KMeans cluster numbers as if they were class labels
• Loading a pickled or joblib model file from a source you do not trust

Udemy and Coursera mostly teach scikit-learn through video lectures, where an instructor builds one notebook from start to finish. Kaggle Learn uses short interactive notebooks in which you write the code yourself. This course does a different job: short narrated lessons followed by questions that make you choose, such as a random forest or gradient boosting, ROC AUC or accuracy, GridSearchCV or RandomizedSearchCV, and say what a piece of code will do before you run it. Video suits watching a whole project once, and notebooks suit typing code. This format builds the judgement to pick the right tool and to notice leakage or a misleading metric before it reaches your results, so it works well next to your own notebook.

Keep a Jupyter notebook open beside the course and run each lesson's example on a small dataset you care about. Change one thing at a time, such as the split, a hyperparameter or the scoring metric, and watch how the numbers move. Answer each quiz question before you read its explanation, because pulling the answer from memory is what makes it stick. Put preprocessing inside a Pipeline from the first week, so that cross-validation and grid search never see the test data. Compare every new model with a majority-class baseline, so you know whether it learned anything at all. When a model looks too good, check for leakage before you celebrate. Once you finish and want more depth on the theory behind these models, Machine Learning: Foundations drills it with concept questions.