Machine Learning with Python - Complete Course & Projects

Explore numpy, a python library for arrays and matrices, with functions for linear algebra and Fourier transforms, and learn array creation, dimensions, and indexing.

Explore numpy arrays with one- and two-dimensional slicing, indexing, and iteration, and apply for loops to perform conditions and math operations, paving the way to matrix operations.

Explore regression metrics MSE and RMSE, quantify how far data points deviate from the regression line, and implement these metrics in Python using numpy.

Understand how a confusion matrix visualizes a classifier's performance, with true positives, false positives, true negatives, and false negatives in illness detection. Learn to compute the accuracy score in Python.

Perform feature engineering by creating gender dummy variables and dropping one column, defining X and y. Split data with 30% test, apply standard scaling, and output model accuracy.

Explore simple and multiple linear regression to model the relationship between a scalar response and explanatory variables using a straight-line equation with an intercept and slope, plus an error term.

Explore the k nearest neighbors algorithm, a nonparametric supervised learner that uses proximity to classify or predict data points. See how varying k affects classification decisions with examples.

Explore support vector machines, supervised learning algorithms for classification and regression. Visualize linear and non-linear SVMs with support vectors and the maximum margin hyperplane, and preview Python usage.

This lecture demonstrates support vector classification with grid search on a prepared dataset, covering dummy variables, scaling, train-test split, and kernel tuning (linear, rbf, polynomial) to maximize accuracy.

Apply support vector regression in Python with data prep, scaling, and outlier removal, then tune parameters via grid search and compare mean absolute error and root mean squared error.

Learn how decision trees use an algorithmic approach to split data based on criteria, enabling classification and regression in a nonparametric supervised learning method.

Demonstrates decision tree coding for classification, including preprocessing with dummy variables, one-hot encoding, label encoding, scaling, train-test split, and evaluating accuracy of 0.86 using a decision tree classifier.

Analyze a booking dataset to predict cancellations using data visualization, feature engineering, and multiple machine learning models, then apply predictions to a new customer scenario.

Import seaborn and matplotlib to visualize data distributions, plot value counts and pie charts for meal plan, room type, and booking status, and select key features for modeling.