Data Science and Machine Learning in Python

Learn Python basics fast by using Python as a calculator, mastering variables, string operations, and zero-based indexing with slicing and common string methods.

Practice practical Python exercises and solutions, covering arithmetic, string operations, and formatting. Learn indexing in lists and dictionaries, domain extraction from emails, and a conditional speed ticket function.

Explore NumPy operations part 3, applying basic inbuilt functions like min, max, argmax, and algebraic operations such as multiply, add, and divide on arrays, with notes on transpose and sorting.

Explore pandas basics: import libraries, load csv data from a bank marketing data set, and inspect head, tail, shape, columns, and dtypes to prepare data for machine learning.

Master Pandas row selection and slicing with iloc, accessing the fifth or last row, and combining row and column lists for targeted data extraction from a bank loan data set.

Learn to compute descriptive statistics in pandas: select columns, perform numeric operations, use describe to summarize central tendency and dispersion, and inspect data types and missing values.

Learn how to handle missing data in pandas: drop or fill missing values, impute with column means or carrier-based group means, and apply cleaning to prepare data for machine learning.

Visualize data distributions in Python using seaborn, distplot with kde and rug options, and customize bins to compare normal and gamma distributions; explore Iris data with grids for multivariate patterns.

Master data preprocessing in python, covering standardization and normalization, scaling methods, and encoding categorical features with one-hot encoding. Explore discrete bucketing and polynomial features for feature engineering.

Explore practical linear regression in Python: load data, preprocess, split training and test sets, fit the model, predict, and interpret coefficients with evaluation metrics on the Boston housing dataset.

Master linear regression through a guided exercise that walks you through steps to build a regression model, learn procedures, and apply the method in practice.

Explore comparing multiple regressors—linear, ridge, lasso, decision tree, k-nearest neighbors, gaussian process, and random forest—on air quality data, evaluated with R2 score, explained variance, and MAE.

Explore decision tree and random forest classifiers on wine quality data, and master model selection with grid search cross-validation to optimize parameters.

Apply support vector machines to classify breast cancer data, use grid search cross-validation to optimize C and gamma, and evaluate improvements with confusion matrix and classification report.

Explore k-means clustering in python, using make blobs to generate toy data, identify cluster centers and labels, and visualize unsupervised grouping in practice.

Explore feature engineering and dimensionality reduction techniques, including creating new features, PCA, ICA, NMF, LDA, and feature selection, to improve model performance on datasets like breast cancer and digits.