AI Roadmap

Step 1: Overview of Artificial Intelligence

Objective: Develop a broad understanding of what AI is and how it fits into the bigger picture.

Key Terms:

• Artificial Intelligence (AI): Systems designed to mimic human-like decision making and problem-solving.
• Machine Learning (ML): A subset of AI where models learn patterns from data.
• Deep Learning: A branch of ML that uses multi-layered neural networks for complex pattern recognition.
• Neural Networks: Computational structures inspired by the brain, consisting of layers of interconnected nodes (neurons).

Focus:

• What problems AI is designed to solve.
• How ML and deep learning relate to and differ from AI as a whole.

Step 2: Core Machine Learning Concepts

Objective: Grasp the main approaches within machine learning and the terminology behind them.

Key Terms:

• Supervised Learning: Training models on labeled data (inputs paired with correct outputs).
• Unsupervised Learning: Discovering patterns in data without pre-assigned labels.
• Reinforcement Learning (RL): Training an agent to interact with an environment via rewards and penalties.

Additional Concepts:

• Loss Function
• Gradient Descent
• Backpropagation
• Activation Functions

Step 3: Diving into Supervised Learning

• Linear Regression
• Logistic Regression
• Training, Testing, and Validation Sets
• Overfitting
• Regularization

Step 4: Exploring Neural Networks and Deep Learning

• Perceptron
• Layers (Input, Hidden, Output)
• Weights & Biases
• Forward Propagation
• Backpropagation
• Optimizers

Step 5: Reinforcement Learning Fundamentals

• Agent
• Environment
• Policy
• Reward
• Value Functions
• Q-Learning and Policy Gradients

Step 6: Broader Concepts and Practical Application

• Data Collection and Preprocessing
• Model Evaluation Metrics
• Advanced Architectures: CNNs, RNNs, LSTMs, Transformers
• Deployment

Final Checkpoint

Before moving deeper into any of these areas, ensure you understand:

• The overall AI landscape and where each concept fits.
• How basic algorithms and models are structured and trained.
• The importance of evaluation and iterative improvement in AI systems.