Explore Python basics by creating variables with dynamic types and using numbers, strings, lists, and dictionaries. Learn to apply arithmetic, comparison, assignment, and slicing with print formatting and string methods.

Master Python loops by comparing while and for loops, using range, indexing, and lists; explore break, continue, and pass to control flow and real-world iterations.

Explore how to define functions, pass positional and keyword arguments, apply default values, and handle variable arguments, then return results and distinguish local and global variables.

Learn how polymorphism uses a single interface to compute areas for different objects, enabling the same code to handle various shapes while remaining easy to modify and maintain.

Explains selecting and training linear and logistic regression models with multiple inputs, using weights and bias, and when to add nonlinear features like squared terms or a sigmoid.

Explore gradient descent, learning rate, and loss minimization in linear regression. Compute partial derivatives and update parameters to minimize mean squared error.

Explore logistic regression with a sigmoid output, derive weight updates via partial derivatives, and compare batch, mini-batch, and stochastic gradient descent with learning rate scheduling.

Explore momentum optimization, including velocity updates, epsilon and learning rate, and compare adaptive optimizers like Adagrad, RMSProp, and Adam for faster loss minimization.

Shuffle the data and split into training, validation, and test sets to fairly assess model performance on unseen data, select the best model, and ensure generalization in deep learning.

Delve into multi-class logistic regression, probability distributions, and categorical cross-entropy loss with softmax, using maximum likelihood estimation to derive class probabilities across outputs.

Explore why neural networks surpass logistic regression, learn how hidden layers and weights create complex nonlinear functions, and compare activation functions like sigmoid, tanh, ReLU, and softmax in modeling.

Learn automatic differentiation and backpropagation to train neural networks, from forward propagation through linear layers to gradient descent optimization with activation functions and loss.

Explore validation and testing of a neural network on multiclass classification, analyzing training versus validation loss and accuracy, and strategies to address underfitting by adjusting epochs and hidden units.

Avoid zero initialization by using random, zero-mean weight initialization. Xavier initialization sets weight variance to 1 divided by the number of neurons (considering the previous and next layer sizes).

Begin training with a relatively high learning rate to converge quickly, then decay the learning rate as training progresses to reach better minima and improve model performance.