Introduction to sigmoid logistic regression function that gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1

In this tutorial, we'll learn how to reshape arrays, normalize rows, what is broadcasting, and softmax. All of this is extremely useful in machine learning

When we are writing machine learning functions, we must be sure that our code is computationally efficient so we always use vectorization

Logistic regression is a binary classification method. In this full tutorial, we will start writing an algorithm that could predict the correct animal in a given picture

In this part, we'll build a Logistic Regression using a Neural Network mindset. We'll see that it is actually a straightforward Neural Network model

In this tutorial, we will learn how to update learning parameters (gradient descent). We'll use parameters from the forward and backward propagation

In this tutorial, we will implement the predict() function and we will be able to use w and b to predict the labels for our dataset X

In this tutorial, you will see how the overall model is structured by putting together all the building blocks together in the right order

In order for Gradient Descent to work, we must choose the learning rate wisely. In this part, you will see how the learning rate determines how rapidly we update the parameters