AI Lesson 3 – AI vs ML vs DL | Dataplexa

AI Course

I. AI Foundations
1. Introduction to AI 2. History of AI 3. AI vs ML vs DL 4. Search Algorithms 5. Intelligent Agents 6. Informed & Uninformed Search 7. Constraint Satisfaction 8. Adversarial Search 9. Logic in AI 10. Planning in AI 11. Knowledge Representation 12. Probabilistic AI 13. Bayesian Networks 14. Markov Models 15. AI Ethics 16. AI Applications 17. Intro to PyTorch 18. Intro to TensorFlow 19. AI Workflows 20. Data for AI
II. Machine Learning
21. Introduction to ML 22. ML Workflow 23. Supervised Learning 24. Unsupervised Learning 25. Reinforcement Learning 26. Linear Regression 27. Logistic Regression 28. SVM 29. Decision Trees 30. Random Forest 31. Gradient Boosting 32. XGBoost 33. K-Means 34. Hierarchical Clustering 35. Dimensionality Reduction 36. Feature Engineering 37. Feature Selection 38. Model Evaluation 39. Overfitting vs Underfitting 40. Cross Validation
III. Deep Learning
41. Intro to Deep Learning 42. Neural Networks 43. Activation Functions 44. Backpropagation 45. Loss & Optimizers 46. Regularization 47. CNN Basics 48. CNN Architectures 49. RNN, LSTM, GRU 50. Seq2Seq 51. Transformers 52. Attention 53. Positional Encoding 54. Training DL 55. Hyperparameter Tuning 56. Transfer Learning 57. Autoencoders 58. GAN Basics 59. GAN Applications 60. Model Serving
IV. Natural Language Processing
61. Intro to NLP 62. Text Processing 63. Tokenization 64. Stopwords & Lemmatization 65. BoW & TF-IDF 66. Word Embeddings 67. Sentence Embeddings 68. NLP Classification 69. Sequence Modeling 70. RNN in NLP 71. Transformers in NLP 72. BERT Basics 73. BERT Fine-tuning 74. RoBERTa & DistilBERT 75. Sentiment Analysis 76. NER 77. Machine Translation 78. Text Generation 79. NLP Evaluation 80. NLP Use Cases
V. Computer Vision
81. Intro to CV 82. Image Processing 83. Filters & Edges 84. Convolutions 85. Object Detection 86. Object Tracking 87. Image Segmentation 88. Face Recognition 89. Feature Detection 90. OCR 91. Image Augmentation 92. Generative Vision 93. Vision Transformers 94. Multimodal Models 95. CV Use Cases
VI. LLM Engineering
96. Intro to LLMs 97. Tokenization 98. LLM Architecture 99. Pretraining 100. Fine-tuning 101. RLHF 102. Prompt Engineering 103. Embedding Models 104. Vector Databases 105. LLM Agents 106. Guardrails & Safety 107. AI in Production 108. End-to-End Project

AI vs Machine Learning vs Deep Learning

As you explore the field of Artificial Intelligence, you will frequently encounter the terms AI, Machine Learning, and Deep Learning. These terms are often used interchangeably, which can lead to confusion. While they are closely related, they are not the same. Understanding the difference between them is essential before moving into algorithms, models, and coding.

This lesson clarifies how AI, Machine Learning, and Deep Learning are connected, how they differ in scope, and why modern AI systems rely heavily on Machine Learning and Deep Learning techniques.

Artificial Intelligence: The Broad Goal

Artificial Intelligence is the broadest concept among the three. It refers to the overall goal of creating systems that can perform tasks requiring human intelligence. These tasks include reasoning, decision-making, perception, language understanding, and problem-solving.

AI is not limited to learning systems. Any approach that enables machines to behave intelligently falls under AI. This includes rule-based systems, search algorithms, planning systems, and logical reasoning frameworks. Historically, many early AI systems were built without any learning capability at all.

In simple terms, AI defines what we want machines to do, not necessarily how they do it.

Machine Learning: Learning From Data

Machine Learning is a subset of Artificial Intelligence. Instead of explicitly programming every rule, Machine Learning focuses on building systems that can learn patterns directly from data. These systems improve performance as they are exposed to more examples.

For example, rather than manually writing rules to detect spam emails, a Machine Learning model can be trained on thousands of labeled emails to learn what spam looks like. Over time, the model becomes better at identifying new, unseen spam messages.

Machine Learning introduces concepts such as training data, models, features, predictions, and evaluation. These ideas form the backbone of modern AI development and are central to most practical AI applications today.

Deep Learning: Learning Through Neural Networks

Deep Learning is a specialized subset of Machine Learning. It uses neural networks with many layers to learn complex patterns from large amounts of data. These deep architectures allow models to automatically learn hierarchical representations, from simple patterns to highly abstract concepts.

Deep Learning has driven major breakthroughs in fields such as image recognition, speech recognition, natural language processing, and autonomous systems. Tasks that were once extremely difficult using traditional Machine Learning approaches became achievable with deep neural networks.

However, Deep Learning typically requires large datasets, significant computational power, and careful model design. For this reason, it is often applied when simpler Machine Learning techniques are not sufficient.

How These Concepts Fit Together

The relationship between AI, Machine Learning, and Deep Learning can be viewed as a hierarchy. Artificial Intelligence sits at the top as the broad goal. Machine Learning exists within AI as a way to achieve intelligent behavior through data-driven learning. Deep Learning exists within Machine Learning as a powerful technique for learning complex patterns.

Not all AI systems use Machine Learning, and not all Machine Learning systems use Deep Learning. The choice depends on the problem, data availability, and computational constraints.

Why This Distinction Matters in Practice

Understanding these differences is important because it influences how AI systems are designed and implemented. Some problems can be solved effectively using simple rule-based logic, while others require Machine Learning or Deep Learning approaches.

As you move forward in this course, you will see how theoretical concepts translate into practical workflows. Coding becomes essential when implementing Machine Learning and Deep Learning models, while AI principles guide system design and decision-making at a higher level.

Practice Questions

Practice 1: Which term represents the broad goal of creating intelligent machines?



Practice 2: Which approach focuses on learning patterns directly from data?



Practice 3: Which technique uses multi-layer neural networks?



Quiz

Quiz 1: Which statement best describes Artificial Intelligence?





Quiz 2: What defines Machine Learning?





Quiz 3: Which approach is best suited for very large datasets and complex patterns?





What’s Coming Next

In the next lesson, we will explore search algorithms and problem-solving techniques that form the foundation of many classic AI systems.

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