MACHINE LEARNING TOPICS

7. Ensemble Techniques

• Bagging: Random forests
• Boosting: AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost

8. Time Series Analysis

• LSTM and GRU for time series
• State space models
• ARIMA models

9. Recommendation Systems

• Matrix factorization: SVD, ALS
• Hybrid recommendation systems
• Collaborative filtering

10. Anomaly Detection

• Isolation forests
• One-class SVM
• Autoencoders for anomaly detection

11. Computer Vision

• Image generation with GANs
• Transfer learning in vision
• Image classification, segmentation, and object detection

12. Natural Language Processing

• Pre-processing Text: tokenization, stemming, lemmatization
• Word embedding: Word2Vec, GloVe, FastText
• Sequence frameworks: RNN, LSTM, GRU, Transformers (e.g., BERT, GPT)

13. Fairness and Unfairness in ML

• We consider and scale the faults
• Fairness-aware algorithms
• Understanding and expalinability

14. Model Interpretability and Feasibility

• LIME
• SHAP
• Feature importance

15. Big Data and Scalable ML

• Distributed ML
• Frameworks: Spark MLlib, Dask

16. Optimization and Search

• Gradient descent and its alternative: dynamic, mini-batch, momentum, RMSprop, Adam
• Genetic approaches

17. Transfer and Multi-task Learning

• Knowledge refining
• Few-shot and zero-shot learning

18. Practical Aspects

• Model deployment and serving
• ML workflows and pipelines
• ML tools and libraries: TensorFlow, PyTorch, scikit-learn, Keras

19. Recent Research and Trends

• Neural architecture search
• Self-supervised learning
• Federated learning

20. Ethics in ML

• Security and Data privacy
• Accountable AI
• Responsibility and Transparency

            The above topics in the list provide a brief summary within the area of ML, but we know that the list is not yet completed. Every title can be extended into subtopics, technical areas by creating ML as a promising multifaceted domain.

                  Our expert research team will develop your proposal by identifying the existing research gaps and frame detailed research objectives. We line up authenticity and novelty, by ensuring that your proposal is free from plagiarism so as it replicates a unique research perspective.

Machine Learning Project with Source Code

                               Our well expertise team of developers create the models with the correct source code as we stay updated and well prepared with all resources. So, get all your simulation done by us. Some of the new topics are listed below.

1. Alignment-Free Sequence Comparison: A Systematic Survey from a Machine Learning Perspective
2. HASM quantum machine learning
3. TensorFlow: A system for large-scale machine learning
4. Learn: TensorFlow’s High-level Module for Distributed Machine Learning
5. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
6. Adversarial Machine Learning at Scale
7. Scikit-learn: Machine Learning in Python
8. Enforcing and Discovering Structure in Machine Learning
9. Neural Additive Models: Interpretable Machine Learning with Neural Nets
10. A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
11. AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
12. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
13. Dlib-ml: A Machine Learning Toolkit
14. Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
15. Declarative Machine Learning Systems
16. Benchmarking Automatic Machine Learning Frameworks
17. OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
18. Synthetic Dataset Generation for Adversarial Machine Learning Research
19. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
20. Efficient and Robust Automated Machine Learning