Mastering Machine Learning within Artificial Intelligence

This hands-on machine learning course builds a clear, practical foundation in the field, moving from core ideas to real implementations. We open with what machine learning is, how it works, and the main application areas, then introduce supervised learning through the most accessible gateway: linear regression. You’ll see the contrast between traditional programming and the machine-learning approach, and learn how gradient descent works in practice—comparing full-batch, mini-batch, and stochastic updates. From there we step into the artificial neuron and use it to implement linear regression with a single neuron, cementing intuition before scaling up. A short quiz consolidates the essentials on supervised, unsupervised, and reinforcement learning.We then dive deeper into regression: the algebraic method to ground your math intuition, one-dimensional regression, and the ML training pipeline for linear models, culminating in a truly simple neural network and extensions to multiple dependent variables. Next, in classification, you’ll assemble a deep neural network, define the target classes, and practice training and testing a model—connecting theory to usable evaluation workflows.When moving to sequential data, you’ll learn how to predict hourly temperature with an RNN in PyTorch. In a dedicated lesson built with Cursor and AI prompting, you start from a detailed specification and use Cursor to generate a complete, runnable script: you’ll fetch real historical weat