AI & Machine Learning
AI and machine learning become much easier to learn when you follow a sequence. Start with programming and data handling, then learn the mathematics you actually need, build classical machine learning projects and only then move into deeper or generative AI topics.
The roadmap below focuses on practical skills. You do not need to master every library before building projects, but you should understand what your code is doing and how to evaluate whether a model is useful.
Stage 1: Python programming fundamentals
Learn variables, conditions, loops, functions, collections, files, exceptions and modules. Then practise writing small scripts without copying complete solutions.
- Python syntax and functions
- Lists, dictionaries and sets
- File handling and virtual environments
- Git basics
Stage 2: Data handling and SQL
Machine learning work begins with data. Learn how to clean, filter, join and summarize datasets using pandas and SQL. Visualize patterns before trying to train a model.
- NumPy and pandas
- SQL queries and joins
- Missing values and data types
- Basic visualization
Stage 3: Essential mathematics
You need enough statistics and linear algebra to understand model behaviour, not necessarily an advanced mathematics degree. Learn concepts alongside code so they remain connected to practical use.
- Mean, variance and distributions
- Probability basics
- Vectors and matrices
- Gradients and optimization intuition
Stage 4: Classical machine learning
Start with supervised and unsupervised learning. Learn how to split data correctly, establish a baseline and select metrics that match the problem.
- Linear and logistic regression
- Decision trees and ensemble methods
- Clustering
- Feature engineering
- Train/validation/test splits
Stage 5: Model evaluation
A model is not useful just because training accuracy is high. Learn overfitting, cross-validation, precision, recall, F1, ROC-AUC and regression metrics where appropriate.
- Choose metrics by business problem
- Check class imbalance
- Avoid data leakage
- Compare against simple baselines
Stage 6: Deep learning and generative AI
After the fundamentals, learn neural networks and then move into natural language processing, computer vision or generative AI based on your interests. Understand tokens, embeddings, context and evaluation before relying on frameworks.
- Neural network basics
- PyTorch or TensorFlow fundamentals
- Embeddings and vector search concepts
- Prompting, retrieval and model evaluation for GenAI projects
Stage 7: Build and deploy projects
Projects should demonstrate the full workflow: define a problem, prepare data, train or integrate a model, evaluate it and expose results through an application or API.
- Prediction API with FastAPI or Flask
- Data analysis dashboard
- Classification or forecasting project
- Retrieval-based assistant using a well-defined data source
Avoid common roadmap mistakes
Do not collect courses without building, skip data fundamentals, or judge models only by one score. Keep a project log that explains your decisions and results.
- Build after each learning stage
- Document dataset limitations
- Separate experimentation from production code
- Never expose API keys or private data in repositories