The Supervised Machine Learning Bootcamp

Take a tour of the Jupyter dashboard, managing files and folders, uploading notebooks, and opening items. Use the interactive shell to write code and view output with IPython notebook formats.

Master the Jupyter dashboard by using code and markdown cells, executing with Ctrl+Enter or Shift+Enter, and leveraging keyboard shortcuts to edit, move, and navigate input and output fields.

Install and verify the key data science packages for the bootcamp, including NumPy, Pandas, Matplotlib, Seaborn, glob, and scikit-learn, using conda or pip for data manipulation and visualization.

Explore how naive Bayes probabilities update beliefs with new knowledge through a thought experiment using red and blue balls, illustrating Bayes' theorem.

Learn how the count vector riser tokenizes strings, builds a vocabulary of tokens, and uses fit and transform to produce a sparse matrix of word frequencies.

Explore how accuracy, precision, recall, and the F1 score quantify classifier performance using a confusion matrix and classification report for ham versus spam.

Explore distance metrics in two dimensions using the Minkowski framework, including Manhattan distance and Euclidean distance. Generalize to higher dimensions, connect to the Pythagorean theorem, and preview CNN applications.

Generate a random dataset using make_blobs or make_classification and create inputs with two features and a target. Assemble them into a Pandas data frame for analysis and future visualization.

Visualize a random dataset by scatterplots with hue by class, customize colors and markers, and ensure reproducibility to prepare for a subsequent classification algorithm.

Explore how different k values shape decision regions in a k-nearest neighbors classifier. A single neighbor yields volatile boundaries and overfitting; higher k smooths boundaries but increases bias.

Apply k-nearest neighbors regression to a generated linear dataset, visualize data with matplotlib and seaborn, and predict y from x using 1–4 neighbors, illustrating the averaging mechanism.

Explore the pros and cons of k-nearest neighbors for classification and regression, including distance-based predictions, non-parametric nature, and training, with notes on the curse of dimensionality, dataset size, and standardization.

Explore decision trees, from building blocks like root, nodes, and leaves to pruning techniques, with Python and Esk Learn, and see how they contrast with random forests.

Explore decision trees, a versatile flowchart-like data structure used across operations, research, and decision analysis to reach decisions through yes/no questions, with nodes, branches, and leaf outcomes.

See how well crafted decision trees become machine learning models through supervised training on data, using features, questions, and depth to enable classification and regression with ID3, C4.5, CART.

Discover how decision trees use splits evaluated by Gini impurity and information gain, based on class distributions and misclassification risk. Learn why minimizing impurity and balanced data matter for splits.

Learn to implement a random forest in Python using the glass dataset, including data prep, train-test split, model training, and evaluating with precision, recall, and accuracy.

Discover how support vector machines find the maximal margin hyperplane to separate fraudulent versus non-fraudulent transactions, using support vectors and hard or soft margin concepts.

Code a Python-based support vector classifier on the mushroom dataset to predict poisonous or edible mushrooms, loading with pandas and encoding categorical features, with grid-search cross-validation and a confusion matrix.

Encode categorical features with label and ordinal encoders for x and y, then scale inputs to -1 to 1 with a minimax scaler before training a support vector classifier.

Implement a linear support vector machine to classify data by fitting on training data, with C=1. Apply it to test data scaled to -1 to 1 and preview metrics.

Analyze a linear support vector classifier using a confusion matrix with edible and poisonous labels, and evaluate precision, recall, and F1 to improve poisonous mushroom recall through cross-validation.

Optimize a support vector classifier by tuning kernels, C values, and gamma through cross-validation and grid search to reduce misclassifications and improve model performance.

Explore exploratory data analysis, assess salary distribution and correlations with player statistics, visualize feature relationships with a heat map, and learn how ridge and lasso regularization handles correlated features.

Replace missing salary values in the hitter dataframe by using ridge regression to predict and fill the gaps. Scale predictors, generate predictions, and update the dataframe with the estimated salaries.