Mastering Machine Learning within Artificial Intelligence

Learn to build powerful Machine Learning algorithms in Python using scikit-learn, TensorFlow, PyTorch, and more.

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.

Shifting to unsupervised learning, you’ll first understand and then code both K-Means and DBSCAN, before applying dimensionality reduction techniques and putting them into practice to make high-dimensional data tractable and visualizable. Finally, you’ll explore reinforcement learning: from the core principles to classic control problems like CartPole and LunarLander, and even design a custom environment, learning to reason in terms of states, actions, rewards, and policies. The course wraps up with a concise conclusion to help you review key takeaways and plan your next steps. Throughout, the emphasis is on conceptual clarity, clean code, and repeatable workflows—so you don’t just learn ML, you learn to build with it.