Today I continued my Machine Learning journey and explored two very important concepts: the cost function and gradient descent. At first, these topics felt a bit confusing, but breaking them down into simple ideas helped me understand them better. 📌 Cost Function The cost function is used to measure how accurate or inaccurate a model’s predictions are. A high cost means the model is making large errors A low cost means the model is performing well The main goal in machine learning is to reduce this cost as much as possible to improve the model’s performance. 📉 Gradient Descent Gradient descent is an algorithm used to minimize the cost function. It works by: Starting with initial values for the model parameters Adjusting them step by step Moving in the direction that reduces the error I like to think of it as trying to reach the bottom of a valley, where the lowest point represents the best possible model.…