- 0x00. Linear Algebra
- 0x01. Plotting
- 0x02. Calculus
- 0x03. Probability
- 0x04. Convolutions and Pooling
- 0x05. Advanced Linear Algebra
- 0x06. Multivariate Probability
- 0x07. Bayesian Probability
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
- 0x00. Binary Classification
- 0x01. Multiclass Classification
- 0x02. Tensorflow
- 0x03. Optimization
- 0x04. Error Analysis
- 0x05. Regularization
- 0x06. Keras
- 0x07. Convolutional Neural Networks
- 0x08. Deep Convolutional Architectures
- 0x09. Transfer Learning
- 0x0A. Object Detection
- 0x0D. RNNs
- 0x0E. Time Series Forecasting
- 0x0F. Natural Language Processing - Word Embeddings
- 0x10. Natural Language Processing - Evaluation Metrics
- 0x11. Attention
- 0x12. Transformer Applications
- 0x13. QA Bot
Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. In contrast to Supervised Learning (SL) where data is tagged by a human
- 0x00. Dimensionality Reduction
- 0x01. Clustering
- 0x02. Hidden Markov Models
- 0x03. Hyperparameter Tuning
- 0x04. Autoencoders
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.