What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence where computers use algorithms and statistical models to perform tasks without explicit instructions, relying on patterns and inference instead.

What is a Neural Network?

A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

What is Natural Language Processing (NLP)?

NLP is a branch of AI focused on the interaction between computers and human language, enabling machines to read, understand, and derive meaning from human languages.

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Intro to Reinforcement Learning in AI & Artificial Intelligence

Learn about Intro to Reinforcement Learning in this comprehensive AI & Artificial Intelligence tutorial. Master the fundamental loop of Reinforcement Learning. Learn the roles of the Agent and Environment, understand the relationship between states, actions, and rewards, and discover why maximizing long-term 'Return' is the core objective of every RL system.

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RL Hub

Trial and error.

Quick Quiz //

Which of these is an example of Reinforcement Learning?

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Most AI is taught. Reinforcement Learning learns. By interacting with a world and receiving rewards, an agent discovers the optimal strategy through experience.

1The Feedback Cycle

At the heart of RL is a simple, repeating cycle. An Agent (the AI) looks at the current State of the world. It chooses an Action. The Environment (the world) then updates based on that action and gives the agent a Reward (a numerical signal of success or failure) and a New State. This cycle continues until the task is finished, allowing the agent to learn which actions lead to high rewards and which lead to failure.

2The Long Game

A common mistake is thinking the agent only cares about the next Reward. In reality, RL is about the Return—the sum of all rewards from now until the end of the episode. A chess-playing AI might accept the 'negative reward' of losing a pawn if it leads to the 'high return' of winning the game. This ability to trade short-term loss for long-term gain is what makes RL so powerful for complex strategy and planning.

3The Great Trade-off

One of the unique challenges of RL is the Exploration vs. Exploitation dilemma. Should the agent 'Exploit' what it already knows works to get a steady reward? Or should it 'Explore' new, unknown actions in hopes of finding a even better strategy? Balancing this trade-off is the key to building agents that don't get stuck in 'Local Optima' and can find truly creative solutions to problems.

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Real-World Examples

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Interview Prep

?Frequently Asked Questions

Pascual Vila

Frontend Instructor // Code Syllabus

Common Pitfalls & Errors

The Error //

Data Leakage

# Wrong
scaler.fit(X)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# Correct
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

The Solution //

Never use data from the validation or test sets to train your model. This includes fitting scalers or imputers on the entire dataset before splitting.

The Error //

Overfitting on small datasets

// Solution: Use techniques like Dropout, L2 Regularization, or Early Stopping to prevent the model from overfitting the training data.

The Solution //

Training a complex model (like a deep neural network) on a very small dataset usually leads to memorization instead of generalization. Use simpler models or apply strong regularization.