AI 101: How to build an AI from scratch

Define the problem upfront by understanding the business need and breaking it down. Identify inputs and outputs, set performance metrics, and research existing solutions and constraints to tailor ai.

Collect and clean data lay the foundation for building an ai, identifying sources, verifying accuracy, and structuring data in tabular form for training, validation, and testing, while anonymizing sensitive information.

Choose the right algorithm for your ai from scratch course by comparing supervised, unsupervised, and reinforcement learning, including deep learning, cnn, and rnn.

Explore neural networks as deep learning algorithms inspired by the brain, with layers of neurons, backpropagation, architecture choices, and training to recognize patterns in data.

Explore how decision trees, a versatile machine learning algorithm for classification and regression, use data prep, fitting, prediction, evaluation, tuning, and visualization across real-world cases.

Random forests build an ensemble of decision trees trained on bootstrap samples with feature selection, combining predictions by averaging to improve accuracy, handle missing values, and support classification and regression.

Explore support vector machines, a supervised learning method for classification and regression that uses a hyperplane in feature space with support vectors to maximize margin and handle non-linearity.

Prepare and clean data, select an appropriate model, and tune hyperparameters to train an AI that makes accurate predictions, then evaluate with metrics like accuracy, precision, recall, and F1 score.

Compare supervised learning with labeled data to predict outputs and evaluate accuracy, precision, and recall, and unsupervised learning to identify patterns in unlabeled data.

Reinforcement learning trains an agent to act in an environment by taking actions and receiving rewards or penalties to learn a policy that maximizes reward, using value-based or policy-based methods.

A Markov decision process is a mathematical framework for modeling decision making under uncertainty, with states, actions, transition probabilities, and rewards, to learn a policy that maximizes expected cumulative reward.

Learn Q-learning, a reinforcement learning method that estimates action quality in an MDP using a Q table, balancing exploration and exploitation toward the optimal policy with alpha and gamma.

Explore temporal difference learning in a model-free setting to estimate state or action values from rewards and future value estimates, using td error to update q-learning and td-zero methods.

Evaluate model performance on a holdout dataset, tune hyperparameters and architectures, and apply regularization and data augmentation. Use grid or random search to refine and monitor production.

Learn how to build an AI using training sets, from collecting and preprocessing data to splitting, training, evaluating, and continuously updating for accuracy.

Explore deployment methods for AI models, including on-premise, cloud, and edge-based approaches, and learn how to optimize, test, deploy, monitor, and secure ARM models for scalable, private, explainable AI systems.