This Machine Learning and Deep Learning beginner course from Python Simplified is designed to help learners understand the core foundations of artificial intelligence in a simple and practical way. The course explains how machines learn, how neural networks work, and how modern AI systems are built using data.
The course begins with the fundamental question of whether machines can think, introducing key concepts such as the Turing Test and the basics of artificial intelligence reasoning. It then moves into different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning.
Students will learn how neural networks function, starting from simple perceptron models to more advanced deep learning architectures. The course explains important concepts such as activation functions, loss functions, and cross-entropy, helping learners understand how AI models improve over time.
A key part of the course focuses on optimization techniques like gradient descent, showing how models are trained using mathematical updates. Learners also gain practical knowledge of building neural networks using Python libraries such as NumPy and Pandas.
The course further introduces datasets like MNIST and explains how machine learning models are trained using real data. It also covers GPU computing and explains the difference between CPU and GPU processing for AI workloads.
By the end of this course, learners will have a strong understanding of machine learning, neural networks, deep learning fundamentals, and how to build basic AI models using Python.
