Data Science for AI and Machine Learning Using Python

Convert the origin column to a category in the pandas dataframe and review basic statistics, correlations (mpg and cylinders, negatively correlated), and kurtosis with its distribution meaning.

learn panda operations to create a temp_col from mpg divided by cylinders, drop columns, reset index, then use np.where and string methods like lower and contains.

Explore graphs in Python to visualize data relationships and distributions, using scatter plots of horsepower vs. mpg, histograms, and box plots, including outlier interpretation.

Begin hands-on regression by loading the data, computing MSE, RMSE, and MAE, and preparing a dataset with categorical columns and robust validation.

Assess normality with histograms, Q-Q plots, and statistical tests, then apply Box-Cox transformation to normalize skewed data, and verify by inverse transformation and comparing original and transformed data.

Build a simple linear regression model in Python, split data into train and test sets, train the model, and evaluate with MAE, RMSE, and R-squared while visualizing residuals.

Explore linear regression on numeric features by preparing data, dropping categorical columns, and evaluating predictions with MAE, RMSE, and R-squared, plus residual analysis and Durbin-Watson checks.

Explore how tree structures drive classification and regression, and how features are selected by information gain, entropy, and gini to split into leaves.

Apply hands-on XGBoost using DMatrix, install xgboost, prepare data, split train-test, and tune core parameters like eval_metric, eta, col_sample, max_depth, and nthread.

Explore how random forest uses bagging and bootstrap samples to build multiple decision trees in parallel and ensemble their results for classification and regression.

Explore unsupervised learning with clustering, PCA, and KNN, and learn how to determine cluster numbers using silhouette and elbow methods, with practical intuition.