Machine Learning Roadmap

Prerequisites

Mathematics

• Linear Algebra
• Calculus
• Probability and Statistics
• Optimization Theory

Programming

• Python Programming
• Data Structures
• Algorithms
• Software Engineering Basics

Tools and Libraries

• NumPy and Pandas
• Scikit-learn
• TensorFlow/PyTorch
• Git and Version Control

Foundational Concepts

Machine Learning Basics

1. Types of Learning
2. Model Evaluation
3. Feature Engineering
4. Cross-validation

Statistical Learning

1. Probability Theory
2. Statistical Inference
3. Hypothesis Testing
4. Regression Analysis

Data Processing

1. Data Cleaning
2. Feature Selection
3. Dimensionality Reduction
4. Data Augmentation

Core Algorithms

Supervised Learning

1. Linear Models
2. Decision Trees
3. Support Vector Machines
4. Neural Networks

Unsupervised Learning

1. Clustering
2. Dimensionality Reduction
3. Anomaly Detection
4. Association Rules

Advanced Methods

1. Ensemble Methods
2. Deep Learning
3. Reinforcement Learning
4. Transfer Learning

Practical Skills

Development

1. Model Development
2. Hyperparameter Tuning
3. Pipeline Building
4. Model Deployment

Tools and Frameworks

1. Deep Learning Frameworks
2. MLOps Tools
3. Cloud Platforms
4. Monitoring Systems

Best Practices

1. Code Organization
2. Documentation
3. Testing
4. Version Control

Advanced Topics

Deep Learning

1. Neural Architectures
2. Computer Vision
3. Natural Language Processing
4. Generative Models

Specialized Areas

1. Reinforcement Learning
2. Meta-Learning
3. Federated Learning
4. Quantum Machine Learning

Production

1. Model Serving
2. Scalability
3. Monitoring
4. Maintenance

Career Development

Projects

1. Personal Projects
2. Open Source Contributions
3. Research Projects
4. Industry Applications

Professional Skills

1. Problem Solving
2. Communication
3. Collaboration
4. Project Management

Industry Knowledge

1. Ethics and Fairness
2. Industry Standards
3. Best Practices
4. Latest Trends