Discover the most searched Machine Learning related terms and optimize your content for maximum reach. Whether you’re a blogger, a webmaster or a seo pro, understanding popular Machine Learning keywords is crucial for connecting with your target audience.
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Explore Top Machine Learning Keywords
Below, you’ll find a curated list of the most searched keywords in the Machine Learning niche, along with their global monthly search volume and CPC on Google.
| Keyword | Search Volume | CPC |
|---|---|---|
| chatgpt | 9140000 | 1.58 |
| openai | 823000 | 1.72 |
| transformers | 673000 | 3.42 |
| hive | 550000 | 1.38 |
| dall-e | 550000 | 1.03 |
| pandas | 550000 | 2.00 |
| coursera | 450000 | 3.16 |
| midjourney | 450000 | 1.88 |
| python | 368000 | 4.25 |
| credit scoring | 368000 | 7.68 |
| text-to-speech | 301000 | 4.13 |
| robotics | 301000 | 3.48 |
| gru | 301000 | 0.00 |
| bard | 246000 | 0.66 |
| stable diffusion | 246000 | 1.70 |
| jax | 201000 | 1.19 |
| luigi | 201000 | 1.69 |
| llama | 201000 | 2.12 |
| boosting | 201000 | 11.85 |
| google colab | 165000 | 2.82 |
| gpt-4 | 165000 | 2.14 |
| api | 165000 | 10.18 |
| big data | 165000 | 7.75 |
| artificial intelligence | 135000 | 3.38 |
| docker | 135000 | 100.00 |
| spark | 135000 | 1.07 |
| accuracy | 110000 | 3.42 |
| mongodb | 110000 | 13.57 |
| algorithm | 110000 | 3.15 |
| pruning | 110000 | 6.59 |
| gpt | 110000 | 1.46 |
| edx | 90500 | 0.95 |
| convolutional neural networks | 90500 | 3.47 |
| glove | 90500 | 7.12 |
| gpt-3 | 90500 | 3.76 |
| kubernetes | 90500 | 3.71 |
| regression | 90500 | 0.00 |
| palm | 90500 | 1.19 |
| stacking | 90500 | 3.21 |
| stan | 74000 | 1.54 |
| jupyter notebook | 74000 | 2.82 |
| chatbots | 74000 | 3.64 |
| kaggle | 74000 | 3.03 |
| data science | 74000 | 9.25 |
| game theory | 60500 | 4.18 |
| dvc | 60500 | 19.21 |
| rest api | 49500 | 57.96 |
| genomics | 49500 | 10.00 |
| machine learning | 49500 | 6.88 |
| yolo | 49500 | 2.32 |
| text generation | 49500 | 3.29 |
| hugging face | 49500 | 3.18 |
| pytorch | 49500 | 4.49 |
| virtual assistants | 40500 | 20.64 |
| linear regression | 40500 | 4.52 |
| tensorflow | 40500 | 5.23 |
| bert | 40500 | 2.49 |
| datacamp | 40500 | 2.01 |
| nlp | 33100 | 3.86 |
| neural networks | 33100 | 5.02 |
| active learning | 33100 | 2.94 |
| logistic regression | 33100 | 6.60 |
| clustering | 33100 | 1.09 |
| etl | 33100 | 10.28 |
| udacity | 33100 | 19.94 |
| data mining | 27100 | 17.10 |
| matplotlib | 27100 | 1.71 |
| gan | 27100 | 3.58 |
| cassandra | 27100 | 8.32 |
| decision trees | 27100 | 15.04 |
| generative adversarial networks | 27100 | 3.58 |
| bioinformatics | 27100 | 5.00 |
| facial recognition | 22200 | 1.58 |
| hadoop | 22200 | 9.72 |
| numpy | 22200 | 4.29 |
| data visualization | 22200 | 11.33 |
| claude | 22200 | 3.83 |
| airflow | 22200 | 6.50 |
| confusion matrix | 18100 | 0.00 |
| xgboost | 18100 | 5.01 |
| deep learning | 18100 | 5.75 |
| natural language processing | 18100 | 5.03 |
| classification | 18100 | 5.83 |
| deepmind | 18100 | 5.80 |
| scikit-learn | 18100 | 4.89 |
| opencv | 18100 | 10.00 |
| r programming | 18100 | 9.43 |
| data lake | 18100 | 7.79 |
| data warehousing | 18100 | 7.27 |
| maé | 18100 | 1.05 |
| neo4j | 14800 | 3.79 |
| roc curve | 14800 | 0.00 |
| reinforcement learning | 14800 | 4.53 |
| risk assessment | 14800 | 7.75 |
| prefect | 14800 | 1.36 |
| k-means clustering | 14800 | 4.04 |
| support vector machines | 14800 | 0.00 |
| principal component analysis | 14800 | 9.61 |
| r-squared | 14800 | 0.00 |
| random forest | 12100 | 0.00 |
| long short-term memory | 12100 | 4.82 |
| lstm | 12100 | 4.82 |
| computer vision | 12100 | 5.58 |
| a/b testing | 12100 | 19.06 |
| kalman filters | 12100 | 0.00 |
| predictive analytics | 12100 | 9.10 |
| nosql | 12100 | 12.66 |
| lda | 12100 | 0.00 |
| seaborn | 12100 | 2.43 |
| gradient descent | 9900 | 0.00 |
| ibm watson | 9900 | 25.56 |
| f1 score | 9900 | 0.00 |
| alphafold | 9900 | 5.17 |
| auc | 9900 | 84.56 |
| sentiment analysis | 9900 | 5.97 |
| automl | 9900 | 7.77 |
| websockets | 9900 | 14.60 |
| text summarization | 8100 | 2.16 |
| causal inference | 8100 | 2.23 |
| autonomous vehicles | 8100 | 5.00 |
| mlops | 8100 | 9.08 |
| customer segmentation | 8100 | 17.18 |
| keras | 8100 | 2.23 |
| mapreduce | 8100 | 15.66 |
| arima | 8100 | 2.44 |
| graph databases | 8100 | 5.63 |
| mlflow | 8100 | 6.50 |
| time series analysis | 6600 | 6.29 |
| word2vec | 6600 | 0.00 |
| one-hot encoding | 6600 | 0.16 |
| online learning | 6600 | 14.98 |
| conversational ai | 6600 | 6.94 |
| knowledge graphs | 6600 | 3.86 |
| aws sagemaker | 6600 | 13.58 |
| weights and biases | 6600 | 0.00 |
| t-sne | 6600 | 9.88 |
| naive bayes | 6600 | 0.00 |
| quantization | 6600 | 0.00 |
| ai in healthcare | 5400 | 7.79 |
| unsupervised learning | 5400 | 6.73 |
| alphago | 5400 | 1.87 |
| transfer learning | 5400 | 5.64 |
| human-robot interaction | 5400 | 3.07 |
| genetic algorithms | 5400 | 7.31 |
| k-nearest neighbors | 5400 | 0.00 |
| federated learning | 5400 | 11.08 |
| anomaly detection | 4400 | 9.11 |
| algorithmic trading | 4400 | 6.57 |
| bagging | 4400 | 0.00 |
| alphazero | 4400 | 2.81 |
| overfitting | 4400 | 0.00 |
| bias-variance tradeoff | 4400 | 0.00 |
| hierarchical clustering | 4400 | 5.98 |
| backpropagation | 4400 | 0.00 |
| supervised learning | 4400 | 5.15 |
| kubeflow | 4400 | 4.23 |
| recurrent neural networks | 4400 | 5.02 |
| hidden markov models | 4400 | 0.00 |
| ethical ai | 3600 | 6.70 |
| recommender systems | 3600 | 12.63 |
| q-learning | 3600 | 4.18 |
| gradient boosting | 3600 | 0.00 |
| quantitative finance | 3600 | 7.42 |
| nvidia ai | 3600 | 4.99 |
| voice cloning | 3600 | 1.60 |
| feature engineering | 3600 | 6.70 |
| differential privacy | 3600 | 6.70 |
| dbscan | 3600 | 0.00 |
| homomorphic encryption | 3600 | 12.96 |
| google brain | 3600 | 4.37 |
| bayesian networks | 3600 | 6.60 |
| lightgbm | 3600 | 0.00 |
| graph generation | 3600 | 4.74 |
| audio processing | 2900 | 2.83 |
| bayesian optimization | 2900 | 0.00 |
| simulated annealing | 2900 | 0.00 |
| language models | 2900 | 8.88 |
| machine translation | 2900 | 3.90 |
| contrastive learning | 2900 | 0.00 |
| topic modeling | 2900 | 5.27 |
| gaussian processes | 2900 | 0.00 |
| collaborative filtering | 2900 | 9.26 |
| multi-armed bandits | 2900 | 0.00 |
| markov chain monte carlo | 2400 | 0.00 |
| image recognition | 2400 | 3.00 |
| time series forecasting | 2400 | 6.03 |
| word embeddings | 2400 | 0.00 |
| agent-based modeling | 2400 | 4.43 |
| transformer architecture | 2400 | 2.03 |
| speech recognition | 2400 | 10.06 |
| speech synthesis | 2400 | 1.78 |
| dimensionality reduction | 2400 | 0.00 |
| data annotation | 2400 | 21.45 |
| social network analysis | 2400 | 7.79 |
| semantic segmentation | 2400 | 7.12 |
| self-supervised learning | 2400 | 7.78 |
| particle filters | 2400 | 3.47 |
| microsoft research | 2400 | 8.91 |
| few-shot learning | 2400 | 8.17 |
| neuromorphic computing | 2400 | 9.20 |
| music generation | 2400 | 2.00 |
| multi-modal learning | 2400 | 0.00 |
| explainable ai | 2400 | 5.40 |
| meta-learning | 2400 | 7.14 |
| adaboost | 2400 | 0.00 |
| data labeling | 1900 | 25.71 |
| hyperparameter tuning | 1900 | 3.26 |
| ai in education | 1900 | 5.67 |
| azure machine learning | 1900 | 14.69 |
| object detection | 1900 | 4.49 |
| data augmentation | 1900 | 13.35 |
| grid search | 1900 | 0.00 |
| semi-supervised learning | 1900 | 4.25 |
| named entity recognition | 1900 | 6.66 |
| portfolio optimization | 1900 | 5.41 |
| catboost | 1900 | 0.00 |
| monte carlo tree search | 1600 | 0.00 |
| zero-shot learning | 1600 | 8.57 |
| complex systems | 1600 | 0.91 |
| isolation forest | 1600 | 0.00 |
| matrix factorization | 1600 | 0.00 |
| active learning strategies | 1600 | 5.21 |
| responsible ai | 1600 | 6.59 |
| ai alignment | 1600 | 6.07 |
| feature selection | 1600 | 0.00 |
| ibm research | 1600 | 0.00 |
| market basket analysis | 1600 | 18.75 |
| edge ai | 1600 | 8.29 |
| faster rcnn | 1600 | 0.00 |
| tinyml | 1600 | 4.40 |
| ai safety | 1600 | 14.59 |
| cyclegan | 1300 | 0.00 |
| question answering | 1300 | 1.21 |
| underfitting | 1300 | 7.58 |
| expectation maximization | 1300 | 0.00 |
| representation learning | 1300 | 6.38 |
| quantum machine learning | 1300 | 6.50 |
| apriori algorithm | 1300 | 0.00 |
| graph neural networks | 1300 | 5.68 |
| natural language generation | 1300 | 4.85 |
| ensemble learning | 1300 | 9.32 |
| particle swarm optimization | 1300 | 0.00 |
| data preprocessing | 1300 | 10.86 |
| thompson sampling | 1300 | 0.00 |
| pymc | 1300 | 0.00 |
| ai regulation | 1000 | 45.06 |
| ai in marketing | 1000 | 22.32 |
| knowledge distillation | 1000 | 0.00 |
| variational inference | 1000 | 0.00 |
| sarsa | 1000 | 0.00 |
| graph attention networks | 1000 | 0.00 |
| conformal prediction | 1000 | 0.00 |
| multi-task learning | 1000 | 0.00 |
| proximal policy optimization | 1000 | 0.00 |
| outlier detection | 880 | 6.15 |
| style transfer | 880 | 3.03 |
| network science | 880 | 4.15 |
| graph convolutional networks | 880 | 0.00 |
| instance segmentation | 880 | 4.95 |
| attention mechanism | 880 | 0.00 |
| label encoding | 880 | 0.00 |
| contextual bandits | 880 | 0.00 |
| random search | 880 | 0.00 |
| domain adaptation | 880 | 0.00 |
| continual learning | 880 | 8.30 |
| entity resolution | 880 | 13.72 |
| node2vec | 880 | 0.00 |
| bias in ai | 720 | 4.78 |
| time series anomaly detection | 720 | 9.11 |
| adversarial machine learning | 720 | 8.89 |
| probabilistic graphical models | 720 | 0.00 |
| evolutionary algorithms | 720 | 0.00 |
| swarm robotics | 720 | 7.67 |
| synthetic data generation | 720 | 17.33 |
| one-shot learning | 720 | 8.28 |
| pose estimation | 720 | 5.85 |
| ensemble methods | 720 | 0.00 |
| graph clustering | 720 | 0.00 |
| ai in finance | 720 | 10.00 |
| neural architecture search | 720 | 22.58 |
| ai in manufacturing | 720 | 15.34 |
| sarima | 720 | 0.00 |
| uncertainty quantification | 720 | 0.00 |
| text classification | 720 | 14.98 |
| policy gradients | 720 | 0.00 |
| association rules | 720 | 0.00 |
| interpretable machine learning | 720 | 5.96 |
| muzero | 590 | 0.00 |
| reinforcement learning algorithms | 590 | 4.84 |
| probabilistic machine learning | 590 | 5.94 |
| video generation | 590 | 3.40 |
| google cloud ai | 590 | 51.86 |
| content-based filtering | 590 | 21.82 |
| graph embedding | 590 | 0.00 |
| ai governance | 590 | 8.00 |
| intel ai | 590 | 6.13 |
| dcgan | 590 | 0.00 |
| ai in agriculture | 480 | 6.13 |
| mlops tools | 480 | 8.85 |
| amazon machine learning | 480 | 8.61 |
| model evaluation | 480 | 4.80 |
| facebook ai research | 480 | 2.67 |
| change point detection | 480 | 13.30 |
| part-of-speech tagging | 480 | 0.00 |
| feature scaling | 480 | 0.00 |
| model deployment | 480 | 15.00 |
| model monitoring | 480 | 15.00 |
| ai in retail | 390 | 20.70 |
| deep q-networks | 390 | 6.72 |
| probabilistic programming | 390 | 7.45 |
| hyperparameter optimization | 390 | 2.80 |
| community detection | 390 | 0.00 |
| multi-agent systems | 390 | 0.00 |
| churn prediction | 390 | 12.19 |
| emotion detection | 320 | 0.00 |
| link prediction | 320 | 0.00 |
| ai policy | 320 | 5.30 |
| apple machine learning | 320 | 13.03 |
| ai auditing | 260 | 10.81 |
| bayesian deep learning | 260 | 4.29 |
| pig | 260 | 1.36 |
| dialogue systems | 210 | 0.00 |
| data augmentation techniques | 210 | 8.09 |
| monte carlo dropout | 170 | 0.00 |
| fairness in machine learning | 170 | 4.72 |
| graph classification | 170 | 0.00 |
| ai risk management | 170 | 14.60 |
| model compression | 170 | 4.62 |
| ai transparency | 170 | 0.00 |
| ai in transportation | 170 | 6.88 |
| privacy-preserving machine learning | 170 | 39.45 |
| graph sage | 170 | 0.00 |
| extra trees | 140 | 0.00 |
| computer vision for robotics | 110 | 8.23 |
| sentiment classification | 110 | 0.00 |
| hybrid recommender systems | 110 | 8.45 |
| actor-critic methods | 110 | 0.00 |
| drug discovery ai | 90 | 7.62 |
| novelty detection | 90 | 0.00 |
| reinforcement learning in games | 90 | 4.30 |
| anomaly detection techniques | 70 | 9.11 |
| medical imaging ai | 70 | 9.21 |
| ai in energy | 70 | 7.16 |
| protein folding prediction | 70 | 17.00 |
| data drift detection | 50 | 0.00 |
| ai accountability | 50 | 3.13 |
| prophet | 40 | 0.00 |
| concept drift detection | 30 | 5.60 |
| robustness in machine learning | 30 | 0.00 |
| reinforcement learning for finance | 30 | 5.27 |
| cross-modal learning | 20 | 0.00 |
| fraud detection | 10 | 0.00 |
| mean squared error | 10 | 0.00 |
| mask rcnn | 10 | 0.00 |
| deeplearning.ai | 10 | 0.00 |
| reinforcement learning for robotics | 10 | 0.00 |
| cross-validation | 10 | 0.00 |
| ensemble uncertainty | 10 | 0.00 |
| comet.ml | 10 | 0.00 |
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