Machine Learning Fundamentals: Hands-On Hobbyist Path

Configure a working Python 3.11+ environment with NumPy and Jupyter Notebook, then launch a notebook and execute a simple print statement to verify the setup.

Create variables of types int, float, bool, and str, perform arithmetic and string concatenation, and explain the result of type-mixing expressions such as 3 + 2.5.

Write if/elif/else branches and for/while loops that iterate over ranges and lists, then predict the output of a nested loop with a break statement.

Define functions with parameters, default arguments, and return values; distinguish local vs. global scope by tracing variable values before and after a function call.

Create 1-D and 2-D NumPy arrays using np.array, np.arange, and np.random.rand; inspect shape, dtype, and ndim; extract elements and sub-arrays via integer and slice indexing.

Perform element-wise arithmetic, comparison, and universal functions (sqrt, exp) on arrays; explain broadcasting rules by predicting the shape of a (3,1) array added to a (1,4) array.

Build a Jupyter Notebook that generates a reproducible synthetic regression dataset: 1000 samples, 5 features from a standard normal distribution, and a target variable y = X @ true_weights + noise; save arrays as datase

Explain the structure of a DataFrame by loading a CSV file and using head, info, and shape to summarize its dimensions, column types, and missing values.

Implement descriptive statistics for numerical and categorical columns using describe, value_counts, and groupby to compare distributions across categories.

Design histograms, box plots, and scatter plots using Seaborn to visually compare feature distributions and pairwise relationships in the dataset.

Evaluate missing data patterns using isnull and heatmaps, then implement imputation or removal strategies appropriate for each column type.

Compare IQR and z-score methods to detect outliers, then implement winsorization or removal for a selected numerical feature.

Create new features through encoding categorical variables, scaling numerical features, and extracting datetime components to prepare a clean modeling dataset.

Produce a Jupyter notebook that loads a raw dataset, performs complete EDA (summary stats, visualizations, missing value treatment, outlier handling, feature engineering), and exports a cleaned CSV with a markdown summar

Distinguish between supervised and unsupervised learning by categorizing 5 real-world problems into the correct learning paradigm with justification

Explain how linear regression models relationships between features and continuous targets using the concept of minimizing prediction error

Compare logistic regression and decision trees for binary classification by identifying their decision boundary shapes on a 2D visualization

Calculate precision and recall from a confusion matrix and explain why accuracy alone is misleading for imbalanced datasets

Diagnose overfitting vs underfitting by interpreting training vs validation loss curves and propose one regularization technique to address each

Apply min-max scaling to numerical features and one-hot encoding to categorical features using scikit-learn transformers on a sample dataset

Construct an end-to-end classification pipeline (preprocessing, model training, evaluation) that achieves >80% accuracy on the UCI Iris dataset and document all design choices in a 1-page report

Explain how a linear equation represents the relationship between a single feature and a continuous target variable.

Calculate the Mean Squared Error (MSE) to quantify the difference between predicted and actual target values.

Implement the gradient descent update rule to iteratively adjust the slope parameter and minimize MSE.

Extend the gradient descent implementation to simultaneously optimize both the slope and intercept parameters.

Refactor the scalar gradient descent implementation into vectorized NumPy operations for efficient computation on datasets with multiple samples.

Compute the R-squared metric to assess the proportion of variance in the target variable explained by the linear model.

Build a complete linear regression model from scratch using vectorized gradient descent to predict house prices from square footage, achieving an R-squared score above 0.7 on the test set.

Explain the fundamental difference between regression and classification tasks and identify real-world scenarios suited for binary classification.

Implement the sigmoid function in Python and explain how it maps linear outputs to probabilities between 0 and 1.

Describe how logistic regression uses a linear combination of features to define a decision boundary for binary classification.

Calculate Log Loss for a set of predictions and explain why it penalizes confident wrong predictions more heavily than Mean Squared Error.

Implement one iteration of gradient descent to update logistic regression weights using the Log Loss gradient.

Compute Accuracy, Precision, Recall, and F1-score from a confusion matrix and choose the appropriate metric for an imbalanced dataset scenario.

Build a complete spam detection pipeline: preprocess text data, train a logistic regression model from scratch using gradient descent, and evaluate it achieving >85% F1-score on a held-out test set.

Explain how decision trees recursively partition feature space using impurity measures like Gini index or entropy to make predictions.

Implement a decision tree classifier using scikit-learn on a structured dataset and interpret the resulting tree structure.

Apply max_depth, min_samples_split, and min_samples_leaf to prevent overfitting and evaluate the impact on model generalization.

Explain how bagging and feature randomness in Random Forests reduce variance compared to a single decision tree.

Implement a Random Forest classifier, tune n_estimators and max_features, and compare its performance to a single decision tree using cross-validation.

Explain how gradient boosting sequentially corrects errors using weak learners and implement a HistGradientBoostingClassifier for efficient training.

Design a complete pipeline that trains, evaluates, and compares Decision Tree, Random Forest, and Gradient Boosting models on a hobby-relevant dataset, producing a ranked leaderboard with accuracy, F1-score, and training

Design a data splitting strategy with train, validation, and test sets that prevents data leakage for a given dataset size and problem type.

Implement k-fold and stratified k-fold cross-validation using scikit-learn to obtain robust performance estimates for a classification model.

Diagnose overfitting and underfitting by plotting and interpreting learning curves (training vs. validation score vs. training set size) for a supervised learning model.

Execute grid search and random search with cross-validation to optimize hyperparameters for a given estimator, comparing computational cost and effectiveness.

Apply nested cross-validation to simultaneously tune hyperparameters and estimate generalization error without optimistic bias, contrasting it with standard CV.

Compare multiple candidate models using cross-validated performance metrics and paired statistical tests (e.g., 5x2 CV t-test) to select the best model with confidence.

Build and deliver a complete, validated model pipeline artifact (Jupyter notebook + serialized model) for the UCI Heart Disease dataset that includes: stratified train/val/test split, nested CV for hyperparameter tuning

Formulate a well-scoped machine learning problem statement with measurable success criteria for a hobby project

Define a concrete, hobby-friendly ML project scope (e.g., image classifier for personal photos) and write 3-5 verifiable acceptance checks covering data prep, model training, evaluation, and deployment readiness.

Collect, clean, split, and version-control a project-specific dataset using DVC or Git LFS, documenting data sources, preprocessing steps, and split ratios in a README.

Implement a baseline model (e.g., simple CNN or tabular classifier) with a reproducible training loop using PyTorch or scikit-learn, logging hyperparameters, metrics, and artifacts to MLflow or TensorBoard.

Evaluate the trained model against all predefined acceptance checks, generating a test report with confusion matrices, classification reports, and failure case analysis.

Apply at least two improvement techniques (e.g., data augmentation, hyperparameter tuning, architecture changes) and demonstrate measurable gains on acceptance check metrics versus baseline.

Package the final model into a reusable inference module (ONNX/TorchScript/pickle), write a CLI or Streamlit demo script, and verify it passes all acceptance checks on unseen sample data.