Machine learning (ML) is a wide and often improving area which consists of various kinds of title and sub-fields. Novel ideas for machine learning will be shared to scholars while we also develop projects based on your upcoming ideas. The following are the synopsis of the main topics of ML that we have developed are referred below:
- Basics and Foundations
- Description and types of ML: Supervised, Unsupervised, Reinforcement.
- Optimization and cost functions.
- Bias-variance trade-off.
- Overfitting and regularization.
- Evaluation metrics: accuracy, precision, recall, F1 score, ROC, AUC, and others.
- Unsupervised Learning Algorithm
- Clustering: K-means, hierarchical clustering, DBSCAN
- Dimensionally reduction: PCA, t-SNE, autoencoders
- Association rules: Apriori, Eclat
- Supervised Learning Algorithms
- Linear regression
- Logistic regression
- Decision trees and random forests
- K-nearest neighbours
- Neural networks
- Support vector machines
- Deep Learning
- Neural networks architecture: MLP, CNN. RNN, LSTM, GRU, Transformer.
- Transfer learning and fine-tuning
- Generative models: GANs, VAEs
- Regularization techniques: dropout, batch normalization
- Reinforcement Learning
- Policy gradient methods
- Actor-Critic models
- Q-learning
- Deep Q Networks (DQN)
- Bayesian Learning
- Bayesian networks
- Hidden Markov Models
- Gaussian processes
- Ensemble Techniques
- Bagging: Random forests
- Boosting: AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost
- Time Series Analysis
- LSTM and GRU for time series
- State space models
- ARIMA models
- Recommendation Systems
- Matrix factorization: SVD, ALS
- Hybrid recommendation systems
- Collaborative filtering
- Anomaly Detection
- Isolation forests
- One-class SVM
- Autoencoders for anomaly detection
- Computer Vision
- Image generation with GANs
- Transfer learning in vision
- Image classification, segmentation, and object detection
- Natural Language Processing
- Pre-processing Text: tokenization, stemming, lemmatization
- Word embedding: Word2Vec, GloVe, FastText
- Sequence frameworks: RNN, LSTM, GRU, Transformers (e.g., BERT, GPT)
- Fairness and Unfairness in ML
- We consider and scale the faults
- Fairness-aware algorithms
- Understanding and expalinability
- Model Interpretability and Feasibility
- LIME
- SHAP
- Feature importance
- Big Data and Scalable ML
- Distributed ML
- Frameworks: Spark MLlib, Dask
- Optimization and Search
- Gradient descent and its alternative: dynamic, mini-batch, momentum, RMSprop, Adam
- Genetic approaches
- Transfer and Multi-task Learning
- Knowledge refining
- Few-shot and zero-shot learning
- Practical Aspects
- Model deployment and serving
- ML workflows and pipelines
- ML tools and libraries: TensorFlow, PyTorch, scikit-learn, Keras
- Recent Research and Trends
- Neural architecture search
- Self-supervised learning
- Federated learning
- 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.
- Alignment-Free Sequence Comparison: A Systematic Survey from a Machine Learning Perspective
- HASM quantum machine learning
- TensorFlow: A system for large-scale machine learning
- Learn: TensorFlow’s High-level Module for Distributed Machine Learning
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
- Adversarial Machine Learning at Scale
- Scikit-learn: Machine Learning in Python
- Enforcing and Discovering Structure in Machine Learning
- Neural Additive Models: Interpretable Machine Learning with Neural Nets
- A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models
- AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
- MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
- Dlib-ml: A Machine Learning Toolkit
- Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
- Declarative Machine Learning Systems
- Benchmarking Automatic Machine Learning Frameworks
- OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
- Synthetic Dataset Generation for Adversarial Machine Learning Research
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- Efficient and Robust Automated Machine Learning