Complete Machine Learning & Reinforcement learning 2023

Minimize the squared error cost function J to improve the hypothesis h(x) with theta parameters. Use contour plots and a bowl-shaped surface to locate the minimum.

discover how initialization affects gradient descent, showing the cost function minimum is reachable regardless of starting point by following the derivative signs.

Explore how shifting initialization on a non-convex function affects gradient descent, highlighting local vs global minima, the role of zero initialization, and the risk of converging to a local minimum.

Master gradient descent in simple linear regression by coupling the hypothesis with the cost function, deriving partials, and updating theta0 and theta1 simultaneously until convergence.

Learn to install and set up the anaconda platform across Windows, macOS, and Linux, choose Python versions, and launch Jupyter notebook for machine learning projects.

Explore Unicode, the global standard for character encoding and internationalization, and learn how hex code points and escapes enable cross-language text handling in code.

Learn to handle data with python iloc by selecting rows and columns, build the feature matrix X and the dependent variable vector y, and read data from different file types.

Split the data into training and test sets to evaluate a simple linear regression model on unseen data, using an 80/20 split and a fixed random state for reproducible learning.

Define the main function in Python, initialize parameters for gradient descent, set features to zero, choose a learning rate and iterations, and call the algorithm function.

Scale data using a standard scaler from scikit-learn to keep Xs and ys within a manageable range, then fit and visualize a simple linear regression model with matplotlib.

Observe how the cost function falls with iterations in a simple linear regression model, visualize the error, and evaluate learning rate and iterations for convergence.

Explain core statistics concepts, including descriptive and inferential statistics, mean, median, mode, and p-values, plus regression significance to support machine learning foundations and future predictions.

R-squared measures the proportion of variance explained by a linear regression, indicating how well the model fits, while warning about residuals and biases.

Explore multiple regression by predicting travel time from two independent variables: miles traveled and number of deliveries, while examining concepts like overfitting, multi-collinearity, and the importance of careful variable selection.

Explore single-variable regressions linking travel time to miles traveled, deliveries, and gas price. Assess models with R, R-squared, adjusted R-squared, standard error, F, and p-values, and identify the best predictor.

Encode categorical data in regression using dummy variables, typically n minus one dummies. See examples with exemplary vs not exemplary high schools and four regions north, south, east, west.

build a multiple linear regression model in excel using data analysis tool pack, with factory cost as the dependent variable and A, B, C as independents; assess p-values and refine.

Learn to perform multiple linear regression in Python to predict stock price using interest rate and unemployment rate, including data import with pandas and assessing linearity via scatter plots.

Define dependent and independent variables, then fit a multiple linear regression in Python to predict stock price from interest rate and unemployment rate, including intercept and coefficients.

Explore preparing a python 3 mlr dataset to predict human life expectancy: load excel data, handle missing values, define features, and encode country with label and one-hot encoding.

Develop a multiple linear regression to predict human life expectancy from pre-processed data, using an 80/20 train-test split, auto dummy encoding, and evaluate with R-squared and standard error.

Apply ridge regression to a real-world regression problem using the Boston housing dataset and feature engineering to control model complexity. Evaluate with a train-test split to detect overfitting.

Explore ridge regression for regularization to control model complexity, reduce overfitting, and improve generalization on the Boston housing data, with alpha tuning and coefficient shrinkage.

In this ridge regression demo, vary alpha to show how regularisation constrains coefficients; higher alpha shrinks coefficient magnitudes, while alpha=0 (no regularisation) yields larger, unstable coefficients.

Explore lasso regression (L1 regularization) for business problems, shrinking coefficients to zero for automatic feature selection and easier interpretation, with alpha tuned.

Examine residuals to evaluate how well a least-squares regression explains data, and use residual plots to decide if a linear model suffices or a nonlinear approach is needed.

Explore polynomial regression and the quadratic model to capture non linear relationships, compare with linear regression, and evaluate using residuals, R-squared, and overfitting cautions.

Learn how to generate nonlinear data and apply polynomial regression in Python, using polynomial features and degree selection, train-test split, and visualization to assess overfitting and model fit.

Explore how decision trees split data for classification and regression, using Gini impurity to build root and leaf nodes, with iris and heart disease examples.

Build a regression decision tree to predict travel time from number of deliveries, miles traveled, and gas price using a shared dataset.

Compare linear and logistic regression, explain why logistic regression needs a new cost function, and show how its cost curve yields a single global minimum for 0/1 outcomes.

Learn to estimate logistic regression parameters theta using a unified cost function, compute the gradient, and iteratively update theta via gradient descent to minimize J(theta) and fit the hypothesis.