Machine Learning with Python

Create a bunch with your own data by importing data from files or databases, shaping the data, and preparing a dataset for machine learning experiments.

Explore practical hyperparameter tuning for k-nearest neighbors using Python, comparing parameter choices like number of neighbors and distance metrics, and employing grid search cross-validation to optimize accuracy.

select optimal parameters, train with the best number of neighbors, then save the trained model using pickle’s dump function, storing it in binary format for later loading and production.

Explore practical machine learning workflows in Python, including working with nearest neighbors, parametric and non-parametric methods, distance metrics like Manhattan and Euclidean, and cross-validation for model tuning.

Explore the mathematics of linear regression, deriving the y = mx + b model, understanding slope and intercept, and applying it to continuous outcomes while noting regression assumptions.

Apply linear regression to predict life span from fertility features, fit the model, interpret the intercept and coefficients, and predict on new data with visualization.

Explore the receiver operating characteristic curve to visualize classifier performance by plotting false positive rate versus true positive rate (sensitivity) and evaluating thresholds and the area under the curve.

Explore support vector machines for classification, emphasizing a clear margin of separation, high-dimensional processing, and memory-efficient use of support vectors.

Explore how to identify linear vs non-linear data, use a linear kernel for linear data, and switch to an rbf kernel to separate circular patterns with a clear margin.

Apply support vector classification to the iris toy dataset using two features, train a linear model, and achieve about 0.8 accuracy; use grid search to tune C and gamma.

Explore preprocessing categorical features in Python machine learning, converting string data to numeric, encoding categories, and selecting features such as reason and country to improve lifespan prediction with linear regression.

Learn to build a pipeline that unites pre-processing, imputation and feature engineering with SelectKBest and ANOVA, and a SVC estimator, including memory caching.

Construct a machine learning pipeline that applies a simple imputer for missing values with the most frequent strategy, then fits an svc estimator with a train-test split.