AI Basics Learning Roadmap: AI, Machine Learning, and Deep Learning
AI Learning Project
AI Basics Learning Roadmap: AI, Machine Learning, and Deep Learning
A practical roadmap for programmers learning AI foundations, including the relationship between AI, machine learning, and deep learning.
AI Learning Project

AI Basics Learning Roadmap: AI, Machine Learning, and Deep Learning

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Level: Beginner Reading time: 8 min
  • AI
  • Machine Learning
  • Deep Learning
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Artificial intelligence is often introduced as if it were a single mysterious subject. If you already know how to program, a better starting point is to build a clear map: which ideas belong together, which tools are just implementation details, and which fundamentals you should practice repeatedly.

This article separates artificial intelligence, machine learning, and deep learning, then gives a practical learning path for programmers. The next posts in this series cover the machine learning workflow, model training and evaluation, neural networks, and a small Python classification project.

After reading it, you should be able to answer three questions: whether AI and machine learning are the same thing, why machine learning needs data and labels, and which foundations to learn next.

1. Start With Three Terms

The first source of confusion is usually this group of terms:

  • Artificial intelligence: the broad goal of making software behave intelligently
  • Machine learning: systems that learn patterns from data instead of relying only on hand-written rules
  • Deep learning: a subset of machine learning that uses multi-layer neural networks

They are not separate boxes. The relationship is nested:

Artificial intelligence
└── Machine learning
    └── Deep learning

That means learning AI does not have to begin with training large models. A more stable path is to understand how data becomes a model, then move into neural networks and large-model applications.

2. Traditional Programming vs. Machine Learning

Traditional programming usually looks like this:

Rules + input data -> output

For example, if you write a leap-year function, the rule is explicitly written by the programmer. The input is a year, and the output is true or false.

Machine learning often looks more like this:

Input data + known answers -> learned rule
New input data + learned rule -> prediction

For spam classification, you probably cannot hand-write every useful rule. A common approach is to collect many emails with labels, then let a model learn which patterns are associated with spam.

This is the most important mindset shift for programmers: your code no longer describes every rule directly. It describes how the program should learn rules from data.

3. The Parts of a Basic AI Project

From an engineering perspective, a small machine learning project usually contains these steps:

  1. Define the problem: classification, regression, clustering, ranking, or something else
  2. Prepare the data: where it comes from, whether labels are reliable, and what each field means
  3. Build features: convert raw records into numbers the model can use
  4. Train the model: let the algorithm adjust parameters from training data
  5. Evaluate results: test the model on data it has not seen during training
  6. Use the model: place predictions inside a script, service, or product workflow

Beginners often focus too much on switching to a stronger model. In real projects, data quality, feature handling, and evaluation design are often more important.

4. What to Learn First

If you already know basic programming, start with these areas:

  • Python basics: functions, lists, dictionaries, modules, virtual environments, and packages
  • Data handling: CSV files, tabular data, missing values, and simple statistics
  • Linear algebra intuition: vectors, matrices, and dot products without heavy proof work at the beginning
  • Probability and statistics intuition: mean, variance, distributions, sampling, and correlation
  • Model evaluation: train/test splits, accuracy, validation, and overfitting

You do not need to finish all of this before practicing. The better approach is to run small examples and fill the gaps as each concept appears.

5. A Practical Learning Order

A useful order for programmers is:

  1. Understand the relationship between AI, machine learning, and deep learning
  2. Learn supervised classification and regression
  3. Understand training, validation, and test data
  4. Learn loss functions, parameters, training epochs, and overfitting
  5. Run a complete classification task with scikit-learn
  6. Then move into neural networks, deep learning frameworks, and large-model applications

This order helps you understand why a model can learn from data before you deal with larger frameworks and more complex model architectures.

6. What Not to Rush

These can wait until the foundations are clearer:

  • Training a large deep learning model as the first project
  • Comparing models before understanding evaluation metrics
  • Copying notebooks without explaining each input and output
  • Confusing API usage with understanding AI fundamentals

Using existing models is valuable, but during foundation learning, the main goal is to understand how data enters a model, how predictions are produced, and how predictions are evaluated.

7. A Simple Self-Check

At this stage, you do not need to derive advanced formulas. You should, however, be able to explain these ideas in your own words:

  • Why the same problem can sometimes be solved with hand-written rules or with machine learning
  • What features and labels are, and what role they play during training
  • Why training data and test data should not be mixed casually
  • Why one run of one model is not enough to prove that a model is reliable

8. How to Read This Series

This first article gives the map. The next articles expand the workflow in order:

The goal is not to memorize as many AI terms as possible. The goal is to take a small problem and clearly explain what the data is, what the target is, what the model learns, and how to verify whether it learned something useful.

Internal links

Related tutorials

Review foundation Welcome to haotianblog: what this bilingual technical site covers

An introduction to the bilingual site, its technical focus, and the kinds of articles and resources it will publish.

 Continue next Machine Learning Workflow

Follow the practical path from data and features to training, prediction, and evaluation.

 Learning path AI Learning Path

Read AI foundations, machine learning, CNN work, traffic defense, and AI security in order.

 Topic hub Student AI Projects

Connect handwritten digits, browser demos, and CNN projects into a portfolio path.

 Knowledge map Knowledge Map

See how this article connects to projects, resources, and learning routes.

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This article is for readers who want a beginner-level guide to AI Basics Learning Roadmap. It takes about 8 min and focuses on AI, Machine Learning, Deep Learning.

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The recommended next step is Machine Learning Workflow, so the article connects into a longer learning route instead of ending as an isolated note.

Does this article include runnable code or companion resources?

This article is primarily explanatory, but the related tutorials point to runnable examples, resources, and project pages.

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Article context

AI Learning Project

A practical route from AI concepts to machine learning workflow, evaluation, neural networks, Python practice, handwritten digits, a CIFAR-10 CNN, adversarial traffic-defense notes, and AI security.

Level: Beginner Reading time: 8 min
  • AI
  • Machine Learning
  • Deep Learning

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Other language version 人工智能基础学习路线：先理解什么是 AI、机器学习和深度学习
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Project timeline

Published posts

  1. AI Basics Learning Roadmap Separate AI, machine learning, and deep learning before going into implementation details.
  2. Machine Learning Workflow Follow the practical path from data and features to training, prediction, and evaluation.
  3. Model Training and Evaluation Understand loss, overfitting, train/test splits, accuracy, recall, and F1.
  4. Neural Network Basics Move from perceptrons to activation, forward propagation, backpropagation, and training loops.
  5. NLP Basics: Understanding Bag of Words and TF-IDF An introduction to the most fundamental text representation methods in NLP: Bag of Words (BoW) and TF-IDF.
  6. RNN Basics: Handling Sequential Data with Memory Understand the core concepts of Recurrent Neural Networks (RNN), the role of hidden states, and their application in NLP.
  7. Transformer Self-Attention Read Q/K/V, scaled dot-product attention, multi-head attention, and positional encoding before exploring LLM internals.
  8. Python AI Mini Practice Run a small scikit-learn classification task and read the experiment output.
  9. Handwritten Digit Dataset Basics Read train.csv, test.csv, labels, and the flattened 28 by 28 pixel layout before training the classifier.
  10. Handwritten Digit Softmax in C Follow the C implementation from logits and softmax probabilities to confusion matrices and submission export.
  11. Handwritten Digit Playground Notes See how the offline classifier was adapted into a browser demo with drawing input and probability output.
  12. CIFAR-10 Tiny CNN Tutorial in C Build and train a small convolutional neural network for CIFAR-10 image classification, then read its loss and accuracy output.
  13. Building a Tiny CIFAR-10 CNN in C: Convolution, Pooling, and Backpropagation A source-based walkthrough of cifar10_tiny_cnn.c, covering CIFAR-10 binary input, 3x3 convolution, ReLU, max pooling, fully connected logits, softmax, backpropagation, and local commands.
  14. High-Entropy Traffic Defense Notes Study encrypted metadata leaks, entropy, traffic classifiers, and a defensive Python chaffing prototype.
  15. AI Security Threat Modeling Build a defense map with NIST adversarial ML, MITRE ATLAS, and OWASP LLM risks.
  16. Adversarial Examples and Robust Evaluation Evaluate clean and perturbed accuracy with an FGSM-style digits experiment.
  17. Data Poisoning and Backdoor Defense Study poison rate, trigger behavior, attack success rate, and training pipeline controls.
  18. Model Privacy and Extraction Defense Measure membership inference signal and surrogate fidelity against a local toy model.
  19. LLM, RAG, and Agent Security Separate instructions from data and enforce tool permissions against indirect prompt injection.

Published resources

  1. Python AI practice code guide The article includes a runnable scikit-learn classification script.
  2. digit_softmax_classifier.c The C source for the handwritten digit softmax classifier.
  3. train.csv.zip Compressed handwritten digit training set with 42000 labeled samples.
  4. test.csv.zip Compressed handwritten digit test set with 28000 unlabeled samples.
  5. sample_submission.csv The official submission format example for checking the final output columns.
  6. submission.csv The prediction file generated by the current C project.
  7. digit-playground-model.json The compact softmax demo model and sample set used by the browser playground.
  8. digit-sample-grid.svg A small handwritten digit preview grid extracted from the training set.
  9. Handwritten digit project bundle Contains the source file, compressed datasets, submission files, browser model, and preview grid.
  10. cifar10_tiny_cnn.c source Single-file C tiny CNN with CIFAR-10 loading, convolution, pooling, softmax, and backpropagation.
  11. model_weights.bin sample weights Model weights generated by one local small-sample run.
  12. test_predictions.csv sample predictions Sample test prediction output from the CIFAR-10 tiny CNN.
  13. CNN project explanation PDF Companion explanation material for the CNN project.
  14. Virtual Mirror redacted code skeleton A redacted mld_chaffing_v2.py control-flow skeleton with secrets, node topology, and target lists removed.
  15. Virtual Mirror stress-test template A redacted CSV template for CPU, memory, peak threads, pulse rate, latency, and error measurements.
  16. Virtual Mirror classifier-evaluation template A CSV template for TP, FN, FP, TN, accuracy, precision, recall, F1, ROC-AUC, entropy, and JS divergence.
  17. Virtual Mirror resource notes Notes explaining why the public resources include only redacted code, test templates, and architecture context.
  18. AI Security Lab README Setup, safety boundaries, and quick-run commands for the AI Security series.
  19. AI Security Lab full bundle Includes safe toy scripts, result CSVs, risk register, attack-defense matrix, and architecture diagram.
  20. AI security risk register CSV risk register template for AI threat modeling and release review.
  21. AI attack-defense matrix Maps attack surface, toy demo, metric, and defensive control into one CSV table.
  22. AI Security Lab architecture diagram Shows threat modeling, robustness, data integrity, model privacy, and RAG guardrails.
  23. FGSM digits robustness script FGSM-style perturbation and accuracy-drop experiment for a local digits classifier.
  24. Data poisoning and backdoor toy script Demonstrates poison rate, trigger behavior, and attack success rate on digits.
  25. Model privacy and extraction toy script Outputs membership AUC, target accuracy, surrogate fidelity, and surrogate accuracy.
  26. RAG prompt injection guard toy script Uses a deterministic toy agent to demonstrate external-data demotion and tool-policy blocking.
  27. Deep Learning topic share card A 1200x630 SVG card for sharing the Deep Learning / CNN topic hub.
  28. Machine Learning From Scratch share card A 1200x630 SVG card for the K-means, Iris, and ML workflow topic hub.
  29. Student AI Projects share card A 1200x630 SVG card for handwritten digits, C classifiers, and browser demos.
  30. CNN convolution scan animation An 8-second Remotion animation showing how a 3x3 convolution kernel scans an input and builds a feature map.

Current route

  1. AI Basics Learning Roadmap Learning path step
  2. Machine Learning Workflow Learning path step
  3. Model Training and Evaluation Learning path step
  4. Neural Network Basics Learning path step
  5. Transformer Self-Attention Learning path step
  6. LLM Visualizer Learning path step
  7. Python AI Mini Practice Learning path step
  8. Handwritten Digit Dataset Basics Learning path step
  9. Handwritten Digit Softmax in C Learning path step
  10. Handwritten Digit Playground Notes Learning path step
  11. CIFAR-10 Tiny CNN Tutorial in C Learning path step
  12. High-Entropy Traffic Defense Notes Learning path step
  13. AI Security Threat Modeling Learning path step
  14. Adversarial Examples and Robust Evaluation Learning path step
  15. Data Poisoning and Backdoor Defense Learning path step
  16. Model Privacy and Extraction Defense Learning path step
  17. LLM, RAG, and Agent Security Learning path step

Next notes

  1. Add more image-classification and error-analysis cases
  2. Turn common metrics into a quick reference
  3. Add more AI security defense experiment notes