139.9K
GRIf you want to learn Al in 2026, here's where to start:
First, build a strong foundation in machine learning before moving into deep learning.
Begin with supervised methods like linear and logistic regression to understand optimization and decision boundaries, then explore KNN, Naive Bayes, decision trees, random forests, gradient boosting, and SVMs to see different modeling assumptions and performance trade-offs.
Next, study unsupervised techniques such as k-means and hierarchical clustering, Gaussian mixture models, and dimensionality reduction methods like PCA, t-SNE, and UMAP to learn how structure can be discovered without labels.
With this in mind, transition to deep learning by learning neural networks and autoencoders, then more specialized architectures like CNNs for vision, RNNs for sequences, transformers and LLMs for language, and diffusion models for generative tasks.
This progression builds intuition step by step, from classical algorithms to modern Al systems.
#machinelearning #deeplearning #statistics #explorepage #viral
@grow.ai.ml
