Machine Learning Home

Introduction

This Machine Learning track on TechLearningPRO takes you from dataset hygiene through classical algorithms, forecasting, and production-minded deployment.

ML systems improve by extracting signal from examples rather than encoding every edge case by hand. You will work in Python with scikit-learn and common MLOps tooling.

Understanding the topic

Modules in recommended order:

• Start Here — vocabulary, problem types, and how ML differs from rule-based code.
• 1 · ML Pipeline — cleaning, EDA, metrics, and tuning.
• 2 · Supervised Learning — regression, classification, trees, SVM, k-NN, Naive Bayes, forests, ensembles.
• 3 · Unsupervised Learning — clustering, embeddings, association rules.
• 4 · Reinforcement Learning — agents, rewards, value methods.
• 5 · Semi-Supervised Learning — learning when labels are scarce.
• 6 · Forecasting — ARIMA-family and smoothing models.
• 7 · Deployment & MLOps — APIs, monitoring, CI/CD for models.

Execution workflow

Best practices

• Notebook every lesson — reading without running code hides bugs in your intuition.
• Log train/validation/test metrics in a table you can compare across experiments.
• After classical ML, deep learning is the natural next specialization.

Summary

End-to-end ML: prepare data, train models, evaluate honestly, deploy with observability.