Analyze central tendency and dispersion to reveal data insights for machine learning with mean, median, mode, range, and standard deviation, plus percentiles, distribution shape, and data types.

Identify outliers with box plots by computing percentiles, Q1 and Q3, and the interquartile range to set lower and upper bounds for informed process analysis.

Learn how hypothesis testing validates extrapolation from sample to population by comparing null and alternate hypotheses using p values and a 0.05 significance level, recognizing type I and II errors.

Explore the analysis of variance to compare means across multiple groups, learn one-factor and two-factor ANOVA, and interpret F tests and p-values using Python and Excel.

Explore how linear algebra expresses data as vectors and learning as transformations, using matrix operations, dot products, and inverses to fit linear regression and scale to larger data.

Explore how matrix multiplication underpins deep learning, linking linear algebra to neural networks with input, hidden, and output layers and weight matrices.

Analyze the jacobian matrix and partial derivatives to see how inputs affect outputs in multivariable models, and use the chain rule with backpropagation to train neural networks via gradient descent.

Contrast machine learning and deep learning by abstraction levels, with ML relying on handcrafted features and DL learning layered representations end to end, via dot products and activations.

Explore the mathematics of logistic regression, including the logit function, probability estimation, and iterative coefficient updates via gradient and learning rate using the Titanic dataset.

Explore convex optimization to find the global minimum of a convex function, illustrated by y = x^2, and apply gradient descent via derivatives to minimize the cost or loss function.

Explore how convolutional layers in CNN extract edges and features from images using kernels, activation, pooling and padding, producing a condensed feature vector for final classification.

Intro to RNN explains how looping networks preserve context with a hidden state, enabling memory for sequential data and tasks like time series forecasting and predicting the next word.

Explore the math behind rnn as the hidden state, or memory, updates from h_{t-1} to h_t using x_t and weights w_h, w_x, then compute y_t with softmax.

Celebrate completing the course by downloading a resource with curated links to other statistics and machine learning courses, available via the instructor's Udemy profile.