Here’s the grouped progression (compact paragraphs + one short example each).

Foundational classical ML

Linear Regression — Models a continuous target as a weighted sum of input features; learned by minimizing squared error. Serves as a baseline and introduces concepts of weights, bias, and loss. Example: predicting house prices from area and number of rooms.
Logistic Regression — Uses a linear combination of inputs passed through a sigmoid to predict binary outcomes; trained with cross-entropy loss. Not a neural net but shares the idea of parameterized features and probabilistic outputs. Example: classifying email as spam or not.

Intro to neural networks

Perceptron / Single-layer NN — Early binary classifier that applies a linear function and threshold; can only separate linearly separable data. Introduces weights and step activations; limited expressivity motivates deeper models. Example: basic pattern classification when classes are separable by a line.
Multi-Layer Perceptron (MLP) — Fully connected network with one or more hidden layers and non-linear activations; can approximate complex functions given enough width/depth. Trained via backpropagation and gradient-based optimizers. Example: tabular data classification or small-scale regression tasks.

Architectures for structured data

Convolutional Neural Network (CNN) — Uses convolutional filters to exploit spatially local patterns and weight sharing; reduces parameters and captures translation-invariant features. Widely used for images and grid-like data. Example: image classification (e.g., cats vs dogs).
Recurrent Neural Network (RNN) & variants (LSTM, GRU) — Processes sequential data by maintaining hidden state across time steps; variants address vanishing gradients to capture longer dependencies. Useful for time series and language sequences. Example: simple language modeling or sequence labeling.

Advanced / modern deep learning

Autoencoder — Encoder-decoder network that compresses input to a lower-dimensional representation and reconstructs it; used for unsupervised representation learning and denoising. Example: dimensionality reduction for anomaly detection.
Generative Adversarial Network (GAN) — Two networks (generator and discriminator) trained adversarially so the generator learns to produce realistic samples; powerful for realistic image synthesis but can be unstable to train. Example: generating photorealistic faces.
Transformer — Uses self-attention to model relationships between all input positions without recurrence or convolutions; highly parallelizable and effective for long-range dependencies. Became dominant in NLP and increasingly in vision. Example: machine translation and large language models.

Training & optimization primitives

Backpropagation — Efficient algorithm to compute gradients of the loss w.r.t. parameters via the chain rule; enables training deep networks. Works with any differentiable layers and underpins gradient-based learning. Example: used when training MLPs, CNNs, and transformers.
Optimizers (SGD, Adam) — Algorithms that update parameters using gradients; variants add momentum, adaptive learning rates, or other heuristics to speed convergence and stabilize training. Example: Adam is a common default for many deep learning tasks.

Regularization & evaluation

Dropout — Randomly disables neurons during training to reduce co-adaptation and overfitting; simple and effective regularizer. Example: applied in MLPs and CNNs to improve generalization.
Batch Normalization — Normalizes layer inputs across a mini-batch to stabilize and accelerate training; can enable higher learning rates. Example: used in deep CNNs to make training faster and more stable.
Loss functions — Measure mismatch between predictions and targets (e.g., MSE for regression, cross-entropy for classification); choice impacts training behavior and performance. Example: cross-entropy for multi-class image classification.

If you want, I can: provide short math intuition for any item, show a tiny code example (PyTorch/TensorFlow) for one algorithm, or make a one-page interview cheat-sheet covering these. Which would you like next?

Optional: pick one of the suggested follow-ups

Learning: Deep Learning