What this course needs. Comfortable Python basics (variables, functions, loops) and a basic feel for math (what a function is, what "more" or "less" means). No proofs and no heavy math — we work through the hard parts gradually.

"Neural network" sounds intimidating, but the idea is simple. Start with a single neuron.

A neuron takes a few inputs (numbers), multiplies each by its weight (also a number), adds it all up, and returns one output. That is it. The weights decide what the neuron "pays attention to".

inputs   →  ×weights  →  sum  →  output
 (0.2, 0.9, 0.1)          →       0.74

One neuron is weak. But connect thousands of them into layers and you get a network that can recognize a cat in a photo or a handwritten digit.

How does it get smart? It learns from examples. You do not write rules like "a cat has whiskers and pointy ears". Instead you show the network thousands of photos with the right answers, and it adjusts its weights on its own until it starts guessing correctly. That weight-adjusting process is called training.

Your project — an image classifier. All course you build a network that recognizes categories you choose (you pick them in the last step — whatever you like: dog breeds, plants, game characters, your hobby).

And most importantly — the accuracy narrative. At first your model will guess almost at random. You watch that accuracy number: add layers — it rises, add the right architecture — it rises more, reuse a pretrained network — it may rise the most (how much depends on your photos). It climbs in jumps and plateaus, and sometimes the honest answer is that an idea did not improve it. All course you watch one number move from "guessing" to "works".