AI & Machine Learning Roadmap
Master artificial intelligence and machine learning concepts to build intelligent systems and models.
What You'll Learn
- Machine learning fundamentals
- Deep learning architectures
- Natural language processing
- Computer vision techniques
- Reinforcement learning
Prerequisites
- Python programming
- Linear algebra
- Calculus
- Statistics and probability
Learning Path Structure
- ML Fundamentals (8-10 weeks)
- Deep Learning (10-12 weeks)
- Specialized Applications (8-10 weeks)
Machine Learning Fundamentals
Master the core concepts and algorithms of machine learning.
Supervised Learning
- Regression
- Linear regression
- Polynomial regression
- Regularization techniques
- Gradient descent
- Classification
- Logistic regression
- Decision trees
- Random forests
- Support vector machines
- Model Evaluation
- Cross-validation
- Metrics and scoring
- Bias-variance tradeoff
- Hyperparameter tuning
Unsupervised Learning
- Clustering
- K-means clustering
- Hierarchical clustering
- DBSCAN
- Gaussian mixtures
- Dimensionality Reduction
- PCA
- t-SNE
- UMAP
- Feature selection
ML Projects
- Classification system
- Regression analysis
- Clustering application
- Feature engineering pipeline
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Frequently Asked Questions
How should I follow this roadmap?
Start from the beginning and progress through each section sequentially. Each section builds upon knowledge from previous ones, so it's important to follow them in order for the best learning experience.
How long will it take to complete?
Completion time varies based on your prior experience and how much time you can dedicate to learning. On average, individuals spend between 3-6 months to complete this roadmap when studying part-time.
Are the resources recommended in the roadmap free?
We include a mix of free and paid resources. Many of the documentation and tutorial resources are completely free, while some of the more comprehensive courses may require payment. We always try to include free alternatives where possible.