Skip to content
ExponaBLOG
Series · Day 100 of 100

Practical AI & Machine Learning

  1. 01
    Machine learning for software engineers, explained simply
    You do not need a PhD to ship useful ML features.
  2. 02
    Setting up a Python ML environment: a step-by-step guide
    The tools you need before writing any model code.
  3. 03
    NumPy basics for ML in Practical AI & Machine Learning
    Arrays are the foundation of every ML library.
  4. 04
    pandas for data preparation: what developers should know
    Load, clean and explore tabular data.
  5. 05
    Exploratory data analysis checklist: a step-by-step guide
    Understand data before modeling it.
  6. 06
    Data visualization for ML: practical tips and trade-offs
    Plots catch problems numbers hide.
  7. 07
    Supervised vs unsupervised learning: a step-by-step guide
    The two main families of machine learning.
  8. 08
    scikit-learn tutorial: your first model, explained simply
    Train, predict and evaluate in a few lines.
  9. 09
    Train, validation and test sets: lessons from production
    Why you need three splits.
  10. 10
    Cross-validation explained: patterns, pitfalls and fixes
    A more reliable estimate of model performance.
  11. 11
    Linear regression explained: what developers should know
    The simplest model — and still useful.
  12. 12
    Logistic regression for classification, explained simply
    A strong, explainable baseline.
  13. 13
    Decision trees and random forests for AI & ML developers
    Powerful models for tabular data.
  14. 14
    Gradient boosting with XGBoost and LightGBM: a deep dive
    The winning approach for most tabular problems.
  15. 15
    Precision vs recall explained: a practical AI & ML guide
    Accuracy lies on imbalanced data. These metrics do not.
  16. 16
    F1 score, ROC and AUC in Practical AI & Machine Learning
    More ways to measure classifiers.
  17. 17
    Regression metrics: MAE, RMSE and R² — a practical guide
    Measure prediction error in useful units.
  18. 18
    Overfitting and underfitting: a practical AI & ML guide
    The core challenge of machine learning.
  19. 19
    Feature engineering that improves models for production
    Better features beat better algorithms.
  20. 20
    Handling missing values: AI & ML lessons from production
    Missing data is information too.
  21. 21
    Encoding categorical features: a practical AI & ML guide
    Turn text categories into numbers.
  22. 22
    Feature scaling: when it matters for AI & ML developers
    Some models care about scale, some do not.
  23. 23
    scikit-learn pipelines: AI & ML best practices explained
    Bundle preprocessing and model into one object.
  24. 24
    Data leakage: the silent model killer, explained simply
    When your model sees the answer during training.
  25. 25
    Hyperparameter tuning in Practical AI & Machine Learning
    Find better settings without wasting compute.
  26. 26
    Imbalanced classification: practical tips and trade-offs
    When the positive class is rare.
  27. 27
    Clustering with k-means: AI & ML lessons from production
    Find groups in unlabeled data.
  28. 28
    Dimensionality reduction with PCA for AI & ML developers
    Compress features while keeping information.
  29. 29
    Anomaly detection for applications: a step-by-step guide
    Find fraud, failures and unusual behavior.
  30. 30
    Time series forecasting basics: best practices explained
    Predict demand, traffic and revenue.
  31. 31
    PyTorch basics for developers: a practical AI & ML guide
    Tensors, autograd and a training loop.
  32. 32
    Neural networks explained simply for AI & ML developers
    Layers of simple functions learning complex patterns.
  33. 33
    Training neural networks well: a practical AI & ML guide
    Practical tips that save days.
  34. 34
    Transfer learning: what every AI & ML developer should know
    Reuse pretrained models for your task.
  35. 35
    NLP basics for developers: practical tips and trade-offs
    Turning text into something models understand.
  36. 36
    Text classification with transformers: a practical guide
    Classify support tickets, reviews or intents.
  37. 37
    Embeddings explained in Practical AI & Machine Learning
    Represent meaning as vectors you can compare.
  38. 38
    Vector databases explained: patterns, pitfalls and fixes
    Store and search embeddings at scale.
  39. 39
    Semantic search with pgvector: a practical AI & ML guide
    Search by meaning using the database you already run.
  40. 40
    RAG implementation tutorial: what developers should know
    Retrieval-augmented generation: let an LLM answer from your data.
  41. 41
    Chunking strategies for RAG: what developers should know
    Chunking decides retrieval quality.
  42. 42
    Improving RAG retrieval: AI & ML lessons from production
    When answers are wrong, retrieval is usually why.
  43. 43
    Calling LLM APIs from a backend: lessons from production
    Production patterns for AI features.
  44. 44
    Prompt engineering for developers for AI & ML developers
    Clear instructions beat clever tricks.
  45. 45
    Structured output from LLMs: what developers should know
    Get JSON you can trust.
  46. 46
    Function calling and tool use: a practical AI & ML guide
    Let models call your code safely.
  47. 47
    Building an AI chat feature: what developers should know
    From prototype to production.
  48. 48
    Evaluating LLM features: AI & ML lessons from production
    You cannot improve what you do not measure.
  49. 49
    Reducing LLM costs in Practical AI & Machine Learning
    AI features can get expensive fast.
  50. 50
    AI agents explained in Practical AI & Machine Learning
    Models that plan and use tools in a loop.