Choose the path by the problem you want to solve

Data science, machine learning, and AI overlap, but they are not interchangeable labels. Data science is a broad practice for learning from data. Machine learning builds systems whose behavior is learned from data. Deep learning is a family of machine-learning methods based on multilayer neural networks. AI is a broader category that includes machine learning as well as other ways of building systems that infer, generate, recommend, or act.

This page is a reading path, not a complete course. Each stage identifies what you should be able to do before moving forward.

Stage 1: Frame the problem and work with data

Outcome: distinguish analysis from prediction, identify the unit of observation and target, and inspect data with reproducible code.

  1. Data Science Fundamentals
  2. Data Science vs. Artificial Intelligence and Machine Learning
  3. Introduction to Data Science with R and Python
  4. Basic Statistics Concepts

Checkpoint: write a short problem statement, data dictionary, exploratory analysis, and list of assumptions. If the question can be answered with a query or descriptive analysis, do that before building a model.

Stage 2: Prepare data without leaking the answer

Outcome: create a reproducible split and preprocessing pipeline while protecting the evaluation set from training-time decisions.

  1. Data Cleaning in Python
  2. Feature Engineering for Machine Learning
  3. Causal Inference and Machine Learning

Checkpoint: document missing values, duplicates, label quality, sampling, preprocessing learned from training data, and any path by which future or target information could leak into features.

Stage 3: Learn the major model families

Outcome: choose a baseline and explain why the task is supervised, unsupervised, forecasting, ranking, or another defined problem.

  1. Supervised Machine Learning Examples
  2. Unsupervised Learning Algorithms
  3. Ensemble Methods in Machine Learning
  4. ARIMA in Python

Checkpoint: compare at least one simple baseline with a candidate model using an evaluation design appropriate to the data. Accuracy alone is not a universal metric.

Stage 4: Add neural networks only when justified

Outcome: understand layers, activation functions, optimization epochs, regularization, and validation before selecting a deep architecture.

  1. Neural Network Basics
  2. Neural Network Activation Functions
  3. Epochs in Machine Learning
  4. Dropout in Neural Networks
  5. Convolutional Neural Network Tutorial

Checkpoint: report the baseline, train/validation/test procedure, metric, uncertainty, failure cases, resource cost, and reason a neural model is warranted.

Stage 5: Study generative AI as a separate system discipline

Outcome: evaluate generated output, grounding, privacy, security, provenance, and human review rather than treating a language model as a conventional classifier.

  1. Generative AI and Large Language Models
  2. LLM Evaluation Metrics
  3. AI in the Workplace

Checkpoint: define representative tasks, factuality and safety tests, human-review boundaries, data handling, incident response, and the evidence required to release or stop the system.

Stage 6: Operate and govern the system

Outcome: connect model quality to deployment, monitoring, ownership, cost, security, and lifecycle risk management.

  1. MLOps Best Practices
  2. AI Model Management
  3. Operationalizing Responsible AI
  4. Cloud Computing for Machine Learning

Checkpoint: produce an owner map, versioned artifact trail, monitoring and rollback plan, access controls, cost model, risk assessment, and decommissioning plan.

How to use this path

  • Start at the earliest checkpoint you cannot yet complete.
  • Reproduce examples with a small, licensed dataset and record package versions.
  • Write the baseline and evaluation plan before choosing an advanced tool.
  • Treat old articles as historical material until their code and claims have a visible review date.
  • Use official language/library documentation for APIs; use Data4AI articles for explanation and synthesis.

Authoritative references