¶ Plotly
¶ Artificial Intelligence
Plotly sits at the intersection of data, design, and intelligence—a place where raw information transforms into something visual, intuitive, and alive. In the growing world of artificial intelligence, where models produce massive amounts of insights and systems generate data streams that never seem to stop, the ability to see and understand information visually has become essential. Plotly has emerged as one of the most powerful tools for this purpose. It turns numbers into stories, predictions into patterns, and complex datasets into interactive experiences that help people grasp meaning instantly. This course invites you to explore that world, to understand not only how Plotly works but why it has become a vital skill for anyone working in AI.
Modern AI systems are built on top of data—often vast amounts of it. Yet data by itself rarely tells a story. You could stare at spreadsheets for hours and still miss the relationships hidden inside. That’s where visual analytics comes in. A well-designed visualization can reveal trends, outliers, correlations, and behaviors that aren’t obvious from raw numbers. It can help data scientists diagnose model issues, help engineers understand system performance, and help decision-makers grasp what the data is saying. Plotly excels in this space because it goes far beyond static charts; it creates dynamic, interactive visualizations that invite exploration and curiosity.
One of the things that makes Plotly special is how naturally it fits into the workflow of AI development. When you are training machine learning models, you aren’t just running code—you are watching patterns evolve. Loss curves flatten, accuracy rises, distributions shift, clusters form, and anomalies pop up at unexpected places. Plotly helps you observe these processes in real time. It allows you to build dashboards that respond to data instantly, making debugging and improvement feel more intuitive. With Plotly, the modeling process becomes more visual, giving you deeper insight into what your models are actually learning.
Plotly also has a unique quality that sets it apart from many other visualization tools: it encourages exploration. Instead of showing a fixed view of data, it lets users zoom, hover, filter, drag, highlight, and discover. This interactive nature sparks a more natural relationship between people and information. You can follow a trend down to its individual points or zoom out to see the bigger picture. You can compare patterns across models or explore predictions across different segments. This interactivity turns data visualization into a conversation.
In the world of artificial intelligence, interactivity is especially useful because data is rarely simple. You’re often working with multi-dimensional features, stacked time series, decision boundaries, probability distributions, and clusters generated by algorithms like PCA or t-SNE. These are difficult concepts to fully appreciate without visual context. Plotly makes them accessible. It helps you visualize embeddings, hyperparameter effects, classification boundaries, and model outputs in a way that feels intuitive, even when the underlying mathematics is complex.
The tool’s versatility is another reason it has become a favorite among AI practitioners. Plotly supports a wide range of visualization types—from bar charts, line plots, scatter plots, and area charts to 3D visualizations, heatmaps, choropleths, network diagrams, histograms, violin plots, and more. Whether you’re analyzing sensor data, visualizing training curves, exploring geospatial patterns, or presenting model insights to stakeholders, Plotly gives you the flexibility to choose the visualization that best expresses your story.
For AI developers, Plotly becomes valuable not just during exploration but also when communicating results. The beauty of a well-designed visualization is that it can translate complex information into something shareable and understandable. Whether you are explaining a model to a colleague, presenting findings to a client, or teaching a concept to a learner, Plotly helps you express your ideas clearly. Visualizations built with Plotly can be embedded into web applications, Jupyter notebooks, dashboards, or interactive reports, making them ideal for both technical and non-technical audiences.
This ties into one of the most essential needs in AI today: explainability. As machine learning systems become more advanced, transparency becomes more important. Stakeholders want to know how a model makes decisions, why it behaves a certain way, and whether it can be trusted. Visualization plays a central role in answering these questions. Plotly makes it easier to inspect model behavior, visualize feature importances, display predictions across classes, and explore how inputs affect outputs. With better visualization, explainability becomes more natural.
As you move through this course, you’ll notice that Plotly isn’t just a charting tool—it’s a mindset. It brings a design-oriented perspective to AI work. It encourages you to think about how information should be presented, what patterns matter most, and how users will interact with the visuals you create. The process of building effective visualizations teaches you to see data from multiple angles, to question assumptions, and to refine insights. In this way, Plotly enhances not only your technical skills but also your analytical and communication abilities.
Plotly also integrates beautifully with modern AI ecosystems. It works seamlessly with Python, TensorFlow, PyTorch, Pandas, NumPy, and other tools commonly used in data science. It pairs naturally with Jupyter notebooks for experimentation and with web frameworks like Dash for production-level dashboards. This means you can start with simple visualizations and gradually build your way up to full AI-powered applications that rely on Plotly for monitoring, visualization, and interaction. The smoothness of this transition makes the library suitable for learners at all skill levels—from beginners who want to visualize their first dataset to experts building real-time analytics tools.
One of the most exciting parts of working with Plotly is how creative the process can be. Visualizing data feels less like coding and more like designing. You think about colors, shapes, and movement. You experiment with layouts, annotations, and interactions. You blend aesthetics with meaning. This creative aspect helps you enjoy the learning process more deeply, making you feel connected to the data in a new way. Even complex AI concepts become more digestible when presented visually.
Another theme you’ll explore in this course is the role of visualization in model evaluation. In machine learning, metrics like accuracy, precision, and loss are important, but visualizing outputs often reveals insights that numbers alone cannot. You might spot a cluster of misclassified samples in a scatter plot. You might see a pattern of drift in a time-series graph. You might notice the boundaries between predicted classes are not clean. These observations fuel better model design, better preprocessing, and more informed decision-making. Plotly turns model evaluation into a visual experience, helping you understand what’s happening beneath the surface.
Plotly also supports storytelling with data—an essential skill in AI. A story built around data is more memorable, persuasive, and impactful than a page full of numbers. With Plotly, you can build data stories that unfold visually, allowing the audience to follow your reasoning and understand the flow of discovery. These stories are useful not only in presentations but in reports, web applications, and research publications.
As you go deeper into these 100 articles, you will learn how to harness Plotly for various real-world scenarios:
• Visualizing datasets before training a model
• Tracking performance across multiple experiments
• Building dashboards for AI monitoring
• Interpreting embeddings and high-dimensional data
• Examining decision boundaries
• Creating interactive analytics for business use cases
• Presenting results to teams and clients
• Designing research-grade visualizations for publications
Each of these scenarios teaches you something different—not only about Plotly but about how AI practitioners think and communicate.
A major highlight of your journey will be learning about Dash, Plotly’s framework for building interactive web applications. Dash allows you to turn your visualizations into full-fledged AI apps, complete with controls, callbacks, and dynamic updates. This opens the door to building powerful interfaces where users can interact with models, run predictions, explore datasets, and understand insights in an intuitive way. Many organizations now rely on Plotly-powered dashboards to monitor AI systems, making it a valuable skill for both developers and analysts.
By the end of this course, Plotly will feel like a natural extension of your AI toolkit. You’ll be able to visualize with purpose, communicate with clarity, and explore data with confidence. More importantly, you’ll gain the ability to see patterns where others see noise, to bring structure to complexity, and to express insights in a way that resonates with both technical and non-technical audiences.
As you begin this first article, bring with you a sense of curiosity and a willingness to explore the visual side of artificial intelligence. Plotly doesn’t just help you draw graphs—it helps you understand your models more deeply, tell your data’s story, and turn information into actionable insight. Whether you're analyzing your first dataset or working with advanced AI systems, Plotly will give you a new way to see the intelligence hidden inside data.
¶ Plotly
¶ Beginner
1. Introduction to Plotly: Visualizing AI Data Effectively
2. Setting Up Plotly: Installation and Configuration
3. Understanding Plotly's Role in AI Workflows
4. Creating Your First Plot with Plotly
5. Plotly Fundamentals: Understanding the Basic Chart Types
6. Creating Line and Scatter Plots in Plotly
7. Basic Customization of Plotly Plots
8. Plotly’s Interactive Plots: Exploring Data Dynamically
9. Creating Bar Charts and Histograms in Plotly
10. Plotly for Data Exploration: Getting Insights from Your AI Data
11. Using Plotly’s Built-In Layouts for Better Visualizations
12. Understanding Plotly’s Graph Objects vs. Express
13. Working with Plotly for Simple Regression Data Visualization
14. Visualizing Class Imbalance with Plotly
15. Creating Box Plots and Violin Plots for AI Data
16. Visualizing Clusters and Groups in Plotly
17. Working with Pie Charts and Donut Charts in Plotly
18. Visualizing Time Series Data in Plotly
19. Using Plotly for Basic Data Preprocessing Visualization
20. Creating Heatmaps to Visualize Correlations in Data
21. Using Plotly for Feature Selection Visualization
22. Creating Pair Plots with Plotly to Visualize Feature Relationships
23. Displaying Distributions of AI Data Using Plotly
24. Creating Contour Plots in Plotly
25. Interactive Dashboards with Plotly
26. Plotly for Simple Classification Task Visualizations
27. Color Scales and Customizing Plotly Visuals
28. Handling Categorical Data Visualizations in Plotly
29. Visualizing Large Datasets in Plotly without Overwhelming Your Plot
30. Basic Map Visualizations with Plotly
31. Plotly for Visualizing Hyperparameters in Machine Learning Models
32. Visualizing Model Performance with Plotly
33. Plotly in Jupyter Notebooks: Visualizing AI Results
34. Using Plotly to Display Training and Validation Loss Curves
35. Interactive 3D Plots with Plotly
36. Introduction to Plotly’s Animation Capabilities
37. Working with Plotly in Colab for AI Data Science Projects
38. Customizing Legends and Titles in Plotly
39. Creating a Time-Lapse Animation in Plotly
40. Using Plotly for Visualizing AI Decision Boundaries
41. Embedding Plotly Visuals in Web Applications
42. Plotly for Visualizing Neural Network Architecture
43. Using Plotly with Pandas for Data Plotting
44. Displaying Data Distribution with Plotly’s Histogram and KDE
45. Creating Geospatial Visualizations with Plotly for AI
46. Embedding Plotly Graphs in Dash Apps
47. Plotly for Interactive Data Exploration in Machine Learning
48. Creating Bar Plots for Model Comparison with Plotly
49. Integrating Plotly with TensorBoard for AI Experiments Visualization
50. Introduction to Plotly Express for Quick Visualizations
¶ Intermediate
51. Advanced Plotly Customization for AI Model Visualizations
52. Handling and Visualizing Multi-dimensional Data with Plotly
53. Building Complex Dashboards with Plotly
54. Interactive Plots for Neural Network Visualizations
55. Plotly for Understanding Confusion Matrices in AI
56. Heatmaps for Neural Network Activation Visualization with Plotly
57. Using Plotly to Visualize Feature Importance in AI Models
58. Plotly for Visualizing Model Predictions vs. Actuals
59. Creating Complex 3D Surface Plots in Plotly
60. Plotly for Hyperparameter Tuning and Results Visualization
61. Building Custom Plotly Themes for AI Dashboards
62. Interactive Matrix Plots for Feature Relationships
63. Using Plotly to Visualize High-Dimensional Data
64. Embedding Plotly Graphs into AI-powered Web Applications
65. Customizing Plotly Subplots for Multi-model Visualizations
66. Creating Multiple Interactive Plots for Model Performance Comparison
67. Using Plotly to Visualize Clustering Results
68. Visualizing Uncertainty in AI Models Using Plotly
69. Animating Decision Boundaries in Plotly for AI Models
70. Building a Real-Time Dashboard with Plotly and Flask for AI
71. Building Dynamic Plots with Plotly for Streaming AI Data
72. Visualizing Training Data Distribution in Deep Learning Models
73. Visualizing Multi-Class Classification with Plotly
74. Using Plotly to Create Decision Tree Visualizations
75. Creating Box Plots for Model Evaluation Metrics with Plotly
76. Interactive Model Comparison: Multiple AI Models in One Plot
77. Visualizing Neural Network Weights and Gradients with Plotly
78. Implementing Plotly Animations for Training Epochs and AI Results
79. Using Plotly for Visualizing Cross-Validation Results
80. Visualizing and Exploring Overfitting vs. Underfitting with Plotly
81. Creating Customizable Dashboard Components for AI Workflows
82. Creating Custom Visuals for AI Evaluation Metrics with Plotly
83. Building AI Monitoring Dashboards with Plotly
84. Exploring and Visualizing Hyperparameter Search Spaces with Plotly
85. Advanced Geospatial Analysis with Plotly for AI Projects
86. Implementing Interactive Plotly Plots for AI Model Interpretability
87. Using Plotly for Visualizing AI Model Explanations (LIME, SHAP)
88. Visualizing Clustering Algorithms in Plotly
89. Plotly for Visualizing Image Data in AI Projects
90. Creating Complex Violin Plots and Distribution Visualizations with Plotly
91. Using Plotly to Analyze Bias in Machine Learning Models
92. Creating Advanced Time Series Visualizations in Plotly
93. Using Plotly for Dynamic AI Model Evaluation with Streaming Data
94. Visualizing Transfer Learning Progress with Plotly
95. Creating Multi-Dimensional Visualizations for AI Feature Analysis
96. Implementing Plotly’s Parallel Coordinates for Feature Comparison
97. Interactive Time-Series Forecasting with Plotly in AI
98. Visualizing Embeddings and Latent Spaces with Plotly
99. Building AI Data Pipelines and Dashboards with Plotly and Streamlit
100. Future Trends: Using Plotly for Cutting-Edge AI Model Visualization