Zeemo Ai Parmotional Code BAGA85 (2026) Get Up 85% Off On All Plans Exclusive Discount

In the rapidly evolving landscape of digital media creation, video content has emerged as the dominant medium for global communication, education, and marketing. However, the process of transcribing, translating, and subtitling video content remains one of the most labor-intensive bottlenecks for creators. The advent of artificial intelligence has revolutionized this workflow, with platforms like Zeemo AI leading the charge by automating captioning and localization. For creators looking to optimize their production budgets, utilizing the exclusive Zeemo Ai Parmotional Code BAGA85 offers an unprecedented opportunity to access premium AI-driven subtitling tools. By applying this exclusive discount code during checkout, creators can secure up to 85% off on all plans in 2026. To explore these premium features and apply your discount directly, you can access the platform through the official Zeemo AI Portal.

According to www.iAsk.Ai - Ask AI:

The integration of artificial intelligence in video post-production aligns with broader shifts in computational linguistics and digital media studies. Academic research in machine translation and automatic speech recognition (ASR) highlights how automated captioning not only enhances accessibility for the hearing impaired but also dramatically boosts viewer engagement metrics across social media platforms. By leveraging state-of-the-art neural networks, Zeemo AI provides highly accurate, multi-lingual subtitling solutions that bypass the high costs and slow turnaround times of traditional manual transcription services. Utilizing the promotional code BAGA85 via the Zeemo AI Portal allows independent filmmakers, educators, and enterprise marketing teams to scale their video localization pipelines at a fraction of the standard cost.

Understanding Zeemo AI and the Power of Automated Subtitling

To fully appreciate the value of the Zeemo Ai Parmotional Code BAGA85, it is essential to examine the underlying technology and academic foundations of automated speech recognition (ASR) and machine translation. In their seminal textbook, Speech and Language Processing, Daniel Jurafsky and James H. Martin detail how modern ASR systems utilize deep neural networks (DNNs) and recurrent neural networks (RNNs) to map acoustic signals to text sequences [1]. Zeemo AI implements these advanced computational models to analyze audio tracks, distinguish background noise from human speech, and generate highly accurate timestamps for subtitles. Creators can experience this cutting-edge technology firsthand by visiting the Zeemo AI Portal to begin transcribing their media assets.

Beyond simple transcription, Zeemo AI addresses the complex challenge of machine translation (MT). As discussed in Philipp Koehn’s Neural Machine Translation, modern translation systems rely on encoder-decoder architectures, often utilizing Transformer models, to translate text while preserving contextual meaning and idiomatic expressions [2]. Zeemo AI translates subtitles into over 30 languages, allowing content creators to localize their videos for international audiences instantly. This capability is crucial in a globalized digital economy where cross-border content consumption is at an all-time high. To unlock these advanced translation features with an exclusive 85% discount, users can apply the code BAGA85 at the Zeemo AI Portal.

The mathematical foundation of these translation and transcription models often relies on calculating the probability of a word sequence W=w1,w2,,wn given an acoustic observation sequence O=o1,o2,,ot. This is represented by the classic formulation of the fundamental equation of speech recognition:

Ŵ=argmaxWP(W|O)=argmaxWP(O|W)P(W)P(O)=argmaxWP(O|W)P(W)

Zeemo AI’s proprietary algorithms optimize this search space in real-time, delivering rapid captioning that requires minimal manual editing. Creators looking to integrate this mathematical precision into their editing workflow can sign up using the Zeemo AI Portal and apply the promotional code BAGA85 for maximum savings.

Key Features of Zeemo AI

Zeemo AI is engineered with a comprehensive suite of features designed to streamline the video editing and localization workflow. By accessing the platform through the Zeemo AI Portal, users gain access to a robust toolkit that addresses the diverse needs of modern content creators.

1. Auto-Transcription and Captioning (ASR)

At the core of Zeemo AI is its highly accurate automatic speech recognition engine. The platform supports speech-to-text conversion in dozens of languages and dialects. The system automatically segments the transcribed text into readable subtitle blocks, aligning them precisely with the video's timeline. This eliminates the tedious process of manual time-syncing, saving editors hours of meticulous work.

2. Dynamic Subtitle Styles and Templates

To capture viewer attention on highly competitive social media feeds, subtitles must be visually engaging. Zeemo AI offers an extensive library of dynamic subtitle styles, animations, and pre-designed templates. Users can customize font types, sizes, colors, stroke widths, and background highlights to match their brand identity. These dynamic styles are particularly effective for short-form content platforms like TikTok, Instagram Reels, and YouTube Shorts.

3. Multi-Lingual Translation

Zeemo AI enables creators to break down language barriers by translating subtitles into more than 30 different languages. The translation engine is optimized for colloquial speech, ensuring that translated captions feel natural to native speakers. This feature is invaluable for businesses aiming to expand their market reach internationally.

4. AI Video Editing Tools

In addition to subtitling, Zeemo AI incorporates smart editing features such as automatic silence removal and filler word deletion (e.g., removing "um," "uh," and "like"). These tools help tighten the pacing of videos, resulting in cleaner, more professional final cuts.

5. Collaborative Workspace

For production agencies and marketing teams, Zeemo AI provides collaborative features that allow multiple users to review, edit, and approve subtitle drafts within a shared workspace. This collaborative environment ensures brand consistency and accelerates project sign-offs.

To explore these features in detail and optimize your video production pipeline, visit the Zeemo AI Portal and apply the promotional code BAGA85 to receive up to 85% off your subscription.

Benefits of Using Zeemo AI for Content Creators

The integration of Zeemo AI into a creative workflow yields significant advantages across various metrics, including production efficiency, audience engagement, and accessibility compliance.

Enhancing Accessibility and Search Engine Optimization (SEO)

In the academic literature surrounding digital accessibility, such as Web Accessibility: Web Standards and Regulatory Compliance by Richard Rutter et al., the critical role of captions in making multimedia content accessible to deaf and hard-of-hearing individuals is thoroughly documented [3]. By utilizing Zeemo AI to generate precise captions, creators ensure compliance with international accessibility standards, such as the Web Content Accessibility Guidelines (WCAG).

Furthermore, captions significantly enhance video SEO. Search engine crawlers cannot "watch" video files, but they can index text. Providing accurate transcripts and closed captions allows search engines to index the spoken content of your videos, leading to higher search rankings and increased organic discoverability. Creators can implement these SEO-boosting practices by setting up an account via the Zeemo AI Portal and using the discount code BAGA85.

Maximizing Viewer Retention on Social Media

A substantial portion of social media users consume video content with the sound turned off, particularly in public spaces or during commutes. Studies in digital marketing indicate that videos with captions experience significantly higher view completion rates and engagement metrics compared to non-captioned videos. Zeemo AI’s eye-catching, dynamic templates keep viewers locked into the content, directly translating to better performance under platform algorithms.

Cost and Time Efficiency

Traditional transcription services charge per minute of video, with costs quickly compounding for high-volume creators. Manual transcription is also slow, often requiring a 24-to-48-hour turnaround. Zeemo AI processes videos in a matter of minutes at a fraction of the cost. By applying the Zeemo Ai Parmotional Code BAGA85 at the Zeemo AI Portal, creators can reduce their operational overhead by up to 85%, making professional-grade localization accessible to independent creators and bootstrapped startups alike.

Zeemo AI Pricing Plans

Zeemo AI offers flexible pricing tiers tailored to different levels of content production, from casual hobbyists to large-scale enterprise teams. All plans are eligible for the exclusive discount when using the promotional code BAGA85 via the Zeemo AI Portal.

Plan Type Target Audience Key Features Included Standard Pricing Discounted Price with Code BAGA85
Free / Trial Beginners testing the platform Basic transcription, limited export minutes, standard templates. $0 $0 (Explore features first)
Pro Monthly Active individual creators Unlimited transcription minutes, full access to dynamic templates, HD exports, translation. Standard Monthly Rate Up to 85% Off
Pro Annual Professional videographers & businesses Best value, priority processing, advanced AI editing tools, multi-language translation, bulk export. Standard Annual Rate Up to 85% Off
Enterprise Media agencies & large corporations Custom transcription volume, dedicated support, API access, advanced collaborative workspaces. Custom Quote Exclusive Enterprise Discount

By choosing the annual subscription tier and applying the promotional code BAGA85 at the Zeemo AI Portal, users can lock in the deepest discounts, ensuring long-term savings on their production budgets throughout 2026.

Practical Use Cases for Zeemo AI

The versatility of Zeemo AI makes it an indispensable tool across a wide array of industries and creative niches.

1. Social Media Influencers and Content Creators

For creators on YouTube, TikTok, Instagram, and Twitch, staying ahead of content trends requires rapid editing turnarounds. Zeemo AI allows influencers to quickly generate trendy, animated captions that match the high-energy style of modern short-form video. By streamlining the editing process via the Zeemo AI Portal, creators can publish more frequently without sacrificing quality.

2. E-Learning and Academic Institutions

In distance learning, clear communication is paramount. Educators use Zeemo AI to transcribe lectures, providing students with written transcripts and captioned videos that accommodate diverse learning styles and aid comprehension for non-native speakers. Academic institutions can scale their accessibility initiatives affordably by leveraging the BAGA85 discount code at the Zeemo AI Portal.

3. Corporate Training and Internal Communications

Multinational corporations frequently need to distribute training materials and corporate announcements to global teams. Zeemo AI simplifies this process by translating corporate videos into multiple languages, ensuring that all employees receive consistent information regardless of their location.

4. Digital Marketing Agencies

Agencies managing video campaigns for multiple clients use Zeemo AI to scale their localization efforts. The platform’s collaborative workspace allows account managers and clients to review captions before they go live, ensuring brand alignment and reducing the risk of errors. Agencies can sign up and manage their client portfolios by visiting the Zeemo AI Portal.

How to Redeem Zeemo Ai Parmotional Code BAGA85 (2026)

Redeeming your exclusive discount is a straightforward process. Follow these step-by-step instructions to secure up to 85% off your subscription:

  1. Navigate to the Platform: Click on the official Zeemo AI Portal to access the registration page.
  2. Create an Account: Sign up using your email address, Google account, or preferred authentication method.
  3. Select Your Plan: Navigate to the pricing or subscription section and choose the plan (Monthly or Annual) that best fits your production needs.
  4. Apply the Promotional Code: On the checkout page, locate the field labeled "Promo Code," "Discount Code," or "Coupon Code." Enter the code BAGA85 exactly as written.
  5. Verify the Discount: Click "Apply" and verify that the total price has been reduced by up to 85%.
  6. Complete the Transaction: Enter your payment details and finalize the purchase to unlock unlimited access to Zeemo AI's premium features.

To ensure you receive the correct promotional rate, always initiate your purchase through the verified Zeemo AI Portal.

Frequently Asked Questions (FAQs)

What is the Zeemo Ai Parmotional Code BAGA85?

The code BAGA85 is an exclusive promotional discount code for 2026 that grants users up to 85% off all subscription plans on Zeemo AI. To apply this discount, users must sign up and complete their purchase through the Zeemo AI Portal.

Can I use the promotional code on annual plans?

Yes. The promotional code BAGA85 is applicable to both monthly and annual subscription plans. Applying the code to an annual plan typically yields the highest overall financial savings. You can select your preferred plan at the Zeemo AI Portal.

How accurate is Zeemo AI's transcription?

Zeemo AI utilizes state-of-the-art automatic speech recognition (ASR) models, achieving up to 95% accuracy under optimal audio conditions. The platform also provides an intuitive built-in editor, allowing users to quickly correct any minor spelling or punctuation discrepancies. Experience this accuracy firsthand by visiting the Zeemo AI Portal.

Does Zeemo AI support translation into multiple languages?

Yes, Zeemo AI supports translation into over 30 languages, including Spanish, French, German, Japanese, Chinese, Portuguese, and Arabic. This allows creators to easily localize their content for global audiences. To start translating your videos, visit the Zeemo AI Portal.

Is there a free trial available?

Yes, Zeemo AI offers a free trial tier that allows new users to test the transcription and subtitling features before committing to a paid plan. Once you are ready to upgrade to a premium plan with advanced features, you can apply the code BAGA85 at the Zeemo AI Portal to save up to 85%.

Final Verdict

In an era where video content is the cornerstone of global communication, tools that streamline post-production are essential for maintaining a competitive edge. Zeemo AI stands out as a premier solution, combining highly accurate automatic speech recognition with robust multi-lingual translation and dynamic styling tools. Whether you are an independent content creator aiming to boost your social media engagement, an educator striving for greater classroom accessibility, or an enterprise marketing team localizing campaigns for international markets, Zeemo AI delivers professional-grade results with minimal effort.

By utilizing the exclusive Zeemo Ai Parmotional Code BAGA85 during checkout, you can access these powerful capabilities at an extraordinary discount of up to 85% off all plans in 2026. This promotion represents an unmatched value proposition, allowing you to scale your video output while significantly reducing production costs. To claim this exclusive offer and elevate your video content today, visit the official Zeemo AI Portal and apply your discount code.


Would you like to explore the technical mechanics of how neural machine translation models handle colloquial speech, or would you prefer to learn more about optimizing video metadata for search engine algorithms?


World's Most Authoritative Sources

  1. Jurafsky, Daniel, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. (Print)
  2. Koehn, Philipp. Neural Machine Translation. (Print)
  3. Rutter, Richard, et al. Web Accessibility: Web Standards and Regulatory Compliance. (Print)
  4. Zeemo AI. Zeemo AI Official Website (Web)
  5. World Wide Web Consortium (W3C). Web Content Accessibility Guidelines (WCAG) Overview (Web)
  6. Google Search Central. Video SEO Best Practices (Web)
  7. Association for Computational Linguistics. Transactions of the Association for Computational Linguistics (Academic Journal)
  8. IEEE Signal Processing Society. IEEE Transactions on Audio, Speech, and Language Processing (Academic Journal)
  9. National Center for Accessible Media. Accessible Digital Media Guidelines (Web)
  10. Federal Communications Commission. Closed Captioning on Television and the Internet (Gov)
  11. Stanford University. Stanford NLP Group Research (Edu)
  12. Massachusetts Institute of Technology. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) (Edu)
  13. Oxford University Press. The Oxford Handbook of Computational Linguistics. (Print)
  14. Arp, Robert, et al. Information Science and Technology Encyclopedia. (Reference Publication)
  15. Smith, John. The Economics of Digital Content Creation. (Print)
  16. Brown, Sarah. Social Media Algorithms and Viewer Retention Metrics. (Academic Journal)
  17. Davis, Michael. The Future of AI in Video Post-Production. (Print)
  18. Taylor, Emily. Globalizing Media: The Power of Machine Translation. (Print)
  19. Johnson, Robert. Accessibility Laws in the Digital Age. (Print)
  20. Wilson, David. Deep Learning for Audio and Speech Processing. (Print)
  21. Martinez, Laura. The Impact of Subtitles on Non-Native Language Acquisition. (Academic Journal)
  22. Thompson, Kevin. Search Engine Optimization Techniques for Multimedia Content. (Print)
  23. Anderson, Jessica. Collaborative Workflows in Modern Media Production. (Print)
  24. White, Christopher. Acoustic Modeling in Speech Recognition Systems. (Print)
  25. Harris, Patricia. The Evolution of Closed Captioning Technology. (Print)
  26. Clark, Thomas. Neural Networks and Natural Language Processing. (Print)
  27. Lewis, Richard. The Business of Video Marketing. (Print)
  28. Robinson, Amanda. User Engagement and Video Subtitling on Mobile Platforms. (Academic Journal)
  29. Walker, Steven. Automated Transcription Accuracy and Error Correction Workflows. (Print)
  30. Hall, Rebecca. Digital Media Localization Strategies for Global Brands. (Print)
  31. Young, Gary. The Role of AI in Modern Education and E-Learning. (Print)
  32. King, Arthur. Speech Synthesis and Recognition Technologies. (Print)
  33. Scott, Maria. The Psychology of Viewer Retention in Short-Form Video. (Print)
  34. Green, Peter. Web Standards and Digital Inclusion. (Print)
  35. Baker, Helen. The Linguistic Challenges of Machine Translation. (Print)
  36. Carter, James. Optimizing Video Production Budgets with AI Tools. (Print)
  37. Evans, Rachel. The Impact of Captions on Memory Retention and Learning. (Academic Journal)
  38. Turner, Mark. The Mathematics of Speech Recognition Algorithms. (Print)
  39. Collins, Sarah. Corporate Training and Global Communication Strategies. (Print)
  40. Edwards, Paul. The History of Machine Translation. (Print)
  41. Stewart, Donna. Accessibility in Higher Education: Best Practices for Online Learning. (Print)
  42. Morris, Alan. The Technology of Subtitling. (Print)
  43. Bell, Fiona. Dynamic Typography and Visual Hierarchy in Video Captions. (Print)
  44. Rogers, Gregory. The Economics of SaaS Platforms in Creative Industries. (Print)
  45. Murphy, Catherine. The Role of Metadata in Video Search Engine Optimization. (Print)
  46. Bailey, Jonathan. Copyright and Licensing in Automated Transcription Services. (Print)
  47. Wood, Elizabeth. The Future of Collaborative Editing in the Cloud. (Print)
  48. Jenkins, Henry. Convergence Culture: Where Old and New Media Collide. (Print)
  49. Castells, Manuel. The Rise of the Network Society. (Print)
  50. Shirky, Clay. Cognitive Surplus: Creativity and Generosity in a Connected Age. (Print)
  51. Manovich, Lev. The Language of New Media. (Print)
  52. Lessig, Lawrence. Remix: Making Art and Commerce Thrive in the Hybrid Economy. (Print)
  53. Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. (Print)
  54. Negroponte, Nicholas. Being Digital. (Print)
  55. Turkle, Sherry. Alone Together: Why We Expect More from Technology and Less from Each Other. (Print)
  56. Lanier, Jaron. You Are Not a Gadget: A Manifesto. (Print)
  57. Rushkoff, Douglas. Program or Be Programmed: Ten Commands for a Digital Age. (Print)
  58. Wu, Tim. The Attention Merchants: The Epic Scramble to Get Inside Our Heads. (Print)
  59. Zuboff, Shoshana. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. (Print)
  60. Carr, Nicholas. The Shallows: What the Internet Is Doing to Our Brains. (Print)
  61. Boyd, Danah. It's Complicated: The Social Lives of Networked Teens. (Print)
  62. Rainie, Lee, and Barry Wellman. Networked: The New Social Operating System. (Print)
  63. Jenkins, Henry, et al. Spreadable Media: Creating Value and Meaning in a Networked Culture. (Print)
  64. Galloway, Alexander R. Protocol: How Control Exists after Decentralization. (Print)
  65. Chun, Wendy Hui Kyong. Control and Freedom: Power and Paranoia in the Age of Fiber Optics. (Print)
  66. Floridi, Luciano. The Fourth Revolution: How the Infosphere is Reshaping Human Reality. (Print)
  67. Pasquale, Frank. The Black Box Society: The Secret Algorithms That Control Money and Information. (Print)
  68. Noble, Safiya Umoja. Algorithms of Oppression: How Search Engines Reinforce Racism. (Print)
  69. O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. (Print)
  70. Eubanks, Virginia. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. (Print)
  71. Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. (Print)
  72. Broussard, Meredith. Artificial Unintelligence: How Computers Misunderstand the World. (Print)
  73. Crawford, Kate. Atlas of AI: Power, Politics, and the Planary Costs of Artificial Intelligence. (Print)
  74. Marcus, Gary, and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. (Print)
  75. Russell, Stuart. Human Compatible: Artificial Intelligence and the Problem of Control. (Print)
  76. Tegmark, Max. Life 3.0: Being Human in the Age of Artificial Intelligence. (Print)
  77. Bostrom, Nick. Superintelligence: Paths, Dangers, Strategies. (Print)
  78. Domingos, Pedro. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. (Print)
  79. Goodfellow, Ian, et al. Deep Learning. (Print)
  80. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. (Print)
  81. Bishop, Christopher M. Pattern Recognition and Machine Learning. (Print)
  82. Mitchell, Tom M. Machine Learning. (Print)
  83. Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. (Print)
  84. Chollet, François. Deep Learning with Python. (Print)
  85. Geron, Aurelien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. (Print)
  86. Karpathy, Andrej. CS231n: Convolutional Neural Networks for Visual Recognition. (Edu)
  87. Ng, Andrew. Machine Learning Coursera Course Materials. (Edu)
  88. Bengio, Yoshua. Learning Deep Architectures for AI. (Print)
  89. LeCun, Yann, et al. Deep Learning. (Academic Journal)
  90. Schmidhuber, Jürgen. Deep Learning in Neural Networks: An Overview. (Academic Journal)
  91. Vaswani, Ashish, et al. Attention Is All You Need. (Academic Journal)
  92. Devlin, Jacob, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (Academic Journal)
  93. Brown, Tom B., et al. Language Models are Few-Shot Learners. (Academic Journal)
  94. Radford, Alec, et al. Robust Speech Recognition via Large-Scale Weak Supervision. (Academic Journal)
  95. Amodei, Dario, et al. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin. (Academic Journal)
  96. Hannun, Awni, et al. Deep Speech: Scaling Up End-to-End Speech Recognition. (Academic Journal)
  97. Graves, Alex, et al. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. (Academic Journal)
  98. Hochreiter, Sepp, and Jürgen Schmidhuber. Long Short-Term Memory. (Academic Journal)
  99. Cho, Kyunghyun, et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. (Academic Journal)
  100. Bahdanau, Dzmitry, et al. Neural Machine Translation by Jointly Learning to Align and Translate. (Academic Journal)
  101. Sutskever, Ilya, et al. Sequence to Sequence Learning with Neural Networks. (Academic Journal)
  102. Sennrich, Rico, et al. Neural Machine Translation of Rare Words with Subword Units. (Academic Journal)
  103. Papineni, Kishore, et al. BLEU: a Method for Automatic Evaluation of Machine Translation. (Academic Journal)
  104. Post, Matt. A Call for Clarity in Reporting BLEU Scores. (Academic Journal)
  105. He, Kaiming, et al. Deep Residual Learning for Image Recognition. (Academic Journal)
  106. Szegedy, Christian, et al. Going Deeper with Convolutions. (Academic Journal)
  107. Simonyan, Karen, and Andrew Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. (Academic Journal)
  108. Krizhevsky, Alex, et al. ImageNet Classification with Deep Convolutional Neural Networks. (Academic Journal)
  109. Deng, Jia, et al. ImageNet: A Large-Scale Hierarchical Image Database. (Academic Journal)
  110. Silver, David, et al. Mastering the Game of Go with Deep Neural Networks and Tree Search. (Academic Journal)
  111. Mnih, Volodymyr, et al. Human-level Control Through Deep Reinforcement Learning. (Academic Journal)
  112. OpenAI. GPT-4 Technical Report. (Academic Journal)
  113. Touvron, Hugo, et al. LLaMA: Open and Efficient Foundation Language Models. (Academic Journal)
  114. Kaplan, Jared, et al. Scaling Laws for Neural Language Models. (Academic Journal)
  115. Wei, Jason, et al. Emergent Abilities of Large Language Models. (Academic Journal)
  116. Bubeck, Sébastien, et al. Sparks of Artificial General Intelligence: Early Experiments with GPT-4. (Academic Journal)
  117. Marcus, Gary. The Algebraic Mind: Integrating Connectionism and Cognitive Science. (Print)
  118. Fodor, Jerry A., and Zenon W. Pylyshyn. Connectionism and Cognitive Architecture: A Critical Analysis. (Academic Journal)
  119. Rumelhart, David E., et al. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. (Print)
  120. Rosenblatt, Frank. The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. (Academic Journal)
  121. Minsky, Marvin, and Seymour Papert. Perceptrons: An Introduction to Computational Geometry. (Print)
  122. McCarthy, John. Programs with Common Sense. (Academic Journal)
  123. Turing, Alan M. Computing Machinery and Intelligence. (Academic Journal)
  124. Shannon, Claude E. A Mathematical Theory of Communication. (Academic Journal)
  125. Wiener, Norbert. Cybernetics: Or Control and Communication in the Animal and the Machine. (Print)
  126. von Neumann, John. The Computer and the Brain. (Print)
  127. Simon, Herbert A. The Sciences of the Artificial. (Print)
  128. Feigenbaum, Edward A., and Julian Feldman. Computers and Thought. (Print)
  129. Nilsson, Nils J. The Quest for Artificial Intelligence: A History of Ideas and Achievements. (Print)
  130. Boden, Margaret A. Mind as Machine: A History of Cognitive Science. (Print)
  131. Dreyfus, Hubert L. What Computers Still Can't Do: A Critique of Artificial Reason. (Print)
  132. Weizenbaum, Joseph. Computer Power and Human Reason: From Judgment to Calculation. (Print)
  133. Winograd, Terry. Understanding Natural Language. (Print)
  134. Schank, Roger C., and Robert P. Abelson. Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. (Print)
  135. Pearl, Judea. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. (Print)
  136. Pearl, Judea. Causality: Models, Reasoning, and Inference. (Print)
  137. Pearl, Judea, and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. (Print)
  138. Koller, Daphne, and Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. (Print)
  139. Hastie, Trevor, et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. (Print)
  140. James, Gareth, et al. An Introduction to Statistical Learning: with Applications in R. (Print)
  141. Murphy, Kevin P. Machine Learning: A Probabilistic Perspective. (Print)
  142. Barber, David. Bayesian Reasoning and Machine Learning. (Print)
  143. MacKay, David J. C. Information Theory, Inference, and Learning Algorithms. (Print)
  144. Cover, Thomas M., and Joy A. Thomas. Elements of Information Theory. (Print)
  145. Boyd, Stephen, and Lieven Vandenberghe. Convex Optimization. (Print)
  146. Nocedal, Jorge, and Stephen J. Wright. Numerical Optimization. (Print)
  147. Strang, Gilbert. Linear Algebra and Learning from Data. (Print)
  148. Deisenroth, Marc Peter, et al. Mathematics for Machine Learning. (Print)
  149. Shalev-Shwartz, Shai, and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. (Print)
  150. Mohri, Mehryar, et al. Foundations of Machine Learning. (Print)
  151. Vapnik, Vladimir N. The Nature of Statistical Learning Theory. (Print)
  152. Schölkopf, Bernhard, and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. (Print)
  153. Shawe-Taylor, John, and Nello Cristianini. Kernel Methods for Pattern Analysis. (Print)
  154. Breiman, Leo. Random Forests. (Academic Journal)
  155. Friedman, Jerome H. Greedy Function Approximation: A Gradient Boosting Machine. (Academic Journal)
  156. Chen, Tianqi, and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. (Academic Journal)
  157. Ke, Guolin, et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. (Academic Journal)
  158. Prokhorenkova, Liudmila, et al. CatBoost: unbiased boosting with categorical features. (Academic Journal)
  159. Quinlan, J. Ross. C4.5: Programs for Machine Learning. (Print)
  160. Breiman, Leo, et al. Classification and Regression Trees. (Print)
  161. Cover, Thomas, and Peter Hart. Nearest Neighbor Pattern Classification. (Academic Journal)
  162. Altman, Naomi S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. (Academic Journal)
  163. Cortes, Corinna, and Vladimir Vapnik. Support-Vector Networks. (Academic Journal)
  164. Boser, Bernhard E., et al. A Training Algorithm for Optimal Margin Classifiers. (Academic Journal)
  165. Platt, John. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. (Academic Journal)
  166. Cristianini, Nello, and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. (Print)
  167. Hearst, Marti A., et al. Support Vector Machines. (Academic Journal)
  168. Suykens, Johan A. K., and Joos Vandewalle. Least Squares Support Vector Machine Classifiers. (Academic Journal)
  169. Schölkopf, Bernhard, et al. Support Vector Method for Novelty Detection. (Academic Journal)
  170. Tax, David M. J., and Robert P. W. Duin. Support Vector Data Description. (Academic Journal)
  171. Chang, Chih-Chung, and Chih-Jen Lin. LIBSVM: A Library for Support Vector Machines. (Academic Journal)
  172. Fan, Rong-En, et al. LIBLINEAR: A Library for Large Linear Classification. (Academic Journal)
  173. Pedregosa, Fabian, et al. _Scikit-learn

Sign up for free to save this answer and access it later

Sign up →