Step into the world of AI and machine learning with Deep Learning with Python, a hands-on course that takes you beyond theory and into building your very own Artificial Neural Network (ANN) from the ground upno black boxes, no shortcuts.
Designed for learners who want to truly understand how deep learning works under the hood, this course walks you through each step of constructing a neural network using Pythonstarting with core concepts and leading up to a fully functional model.
Whether you're an aspiring data scientist, software developer, or AI enthusiast, you'll gain the foundational skills and confidence to build and experiment with deep learning models on your own. Through clear explanations and real coding exercises, youll learn how data flows through a network, how weights and biases are optimized, and how activation functions, loss metrics, and backpropagation all work together to train intelligent systems.
Best of all, youll write every line of code yourselfno frameworks like TensorFlow or PyTorchgiving you complete insight into how deep learning algorithms function at their core.
What Youll Learn: Core concepts of deep learning and neural networks How to structure and code a neural network using only Python and NumPy Understanding forward propagation, activation functions, and error calculation Implementing backpropagation and gradient descent for model training Evaluating model performance and making improvements Applying your network to simple real-world datasets By the end of this course, you'll not only have a working ANN built entirely from scratch, but also a deep understanding of how modern AI systems learn and make decisions.
This is real coding, real learning, and real AI knowledgeperfect for those who want to go deeper than just clicking through pre-built models. No prior deep learning experience requiredjust a solid grasp of Python and a desire to learn. 
