Here are some suggestions for areas and topics of machine learning research:

• 1. Computer vision: Image classification, object detection, image segmentation, and image generation using GANs. Start with benchmark datasets like MNIST, CIFAR-10, and COCO.
• 2. Natural language processing: Sentiment analysis, text classification, named entity recognition, question answering. Useful datasets are Yelp reviews, IMDB movie reviews, and SQuAD.
• 3. Recommender systems: Collaborative filtering, content-based recommendations, matrix factorization. Try movielens or Netflix prize datasets.
• 4. Time series forecasting: Predicting future values based on past data. Apply recurrent neural nets, and LSTMs on finance, weather, and sales datasets.
• 5. Anomaly detection: Identify outliers and anomalies. Use techniques like isolation forests, and local outlier factors on benchmark datasets.
• 6. Reinforcement learning: Solve games like tic-tac-toe, cartpole balancing, and atari games. Look into Q-learning and policy gradients. Start with OpenAI Gym environments.
• 7. Transfer learning: Use pre-trained models like BERT for text, and ResNet for images as a starting point. Fine-tune your own datasets.

I'd recommend starting with simpler datasets and techniques to get a good grasp, before moving to more complex state-of-the-art methods. Focus on fundamentals like data preprocessing, evaluation metrics, loss functions, and optimization. Replicate some existing papers to get hands-on experience. All the best!

Selecting a research area in machine learning can indeed be overwhelming given its vastness and the rate at which it's evolving. However, I can suggest several exciting and promising areas in machine learning that you might want to consider for your research.

Explainable AI (XAI): As AI and machine learning models become more complex, the need for understanding these models – why they make the decisions they do – is becoming increasingly important. This is especially crucial in areas like healthcare, finance, and law where explainability and transparency are necessary. Research could focus on developing methods to make AI more interpretable.

Reinforcement Learning: This is an area of machine learning where an agent learns to behave in an environment by performing actions and seeing the results. It's gaining popularity in fields like robotics, gaming, navigation, and real-time predictions. Developing more efficient algorithms and exploration strategies could be an interesting research area.

Transfer Learning: The idea here is to apply knowledge gained from one problem to another related problem. It's a crucial aspect of deep learning, especially when there's a scarcity of data. The development of new transfer learning methodologies that can generalize better is an active research area.

Fairness and Bias in AI: There's growing concern about machine learning models unintentionally encoding and propagating societal biases. Research can be conducted on how to make machine learning models more fair, unbiased, and ethical.

Federated Learning: This is a machine learning approach where a model is trained across multiple decentralized devices or servers holding local data samples, without exchanging them. It's crucial for privacy-preserving machine learning and is an emerging research area.

Quantum Machine Learning: As quantum computing technology advances, so too does the exploration of how it can be used for machine learning. Research in this area could revolve around creating new quantum algorithms or exploring the effects of quantum computing on existing machine learning algorithms.

Multimodal Learning: It involves building models that can process and relate information from multiple forms of input, like text, images, and audio. Research in this area can enhance the performance of models in complex tasks such as multimedia content analysis, natural language processing, and more.

Given that you're a beginner, I'd recommend starting with the area that most resonates with you and is related to the coursework or projects you've already done. Start by understanding the basics of that area and then gradually delve into the more complex aspects. You can also look at recent papers published in your area of interest to get an idea of current research topics and to identify potential gaps in the research that you could address.

You should go towards recommendation system. It's a good and perfect area of research

Emerging research areas in machine learning encompass federated learning, quantum machine learning, explainable AI, and AI for healthcare. These fields promise transformative impacts on technology, society, and industry, shaping the future of intelligent systems and fostering ethical, responsible AI advancements.

Starting your research work in machine learning as a beginner is an exciting journey. It's important to choose an area that aligns with your interests and career goals. Here are some areas and topics in machine learning that are suitable for beginners:

1. Supervised Learning:

• Classification: Explore topics like image classification, text classification, or sentiment analysis. You can work with popular datasets like MNIST (handwritten digits) or IMDb (movie reviews).
• Regression: Study regression problems such as house price prediction or stock price forecasting.

2. Unsupervised Learning:

• Clustering: Investigate clustering algorithms like K-Means, Hierarchical Clustering, or DBSCAN. Apply them to group similar data points.
• Dimensionality Reduction: Learn about Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualizing high-dimensional data.

3. Natural Language Processing (NLP):

• Text Classification: Work on sentiment analysis, spam detection, or topic categorization using NLP techniques.
• Named Entity Recognition: Identify entities like names, dates, and locations in text data.
• Text Generation: Explore generative models like Recurrent Neural Networks (RNNs) and Transformers to generate text.

4. Computer Vision:

• Image Processing: Start with basic image processing tasks like edge detection, image denoising, or object detection using Convolutional Neural Networks (CNNs).
• Object Recognition: Build models to recognize objects in images or video streams.

5. Reinforcement Learning:

• Classic Control Problems: Begin with simple environments like CartPole or Mountain Car to learn reinforcement learning fundamentals.
• Deep Reinforcement Learning: Advance to more complex tasks like training agents to play video games or navigate robotic systems.

6. Recommender Systems:

• Collaborative Filtering: Learn about user-based and item-based collaborative filtering for building recommendation systems.
• Matrix Factorization: Explore techniques like Singular Value Decomposition (SVD) for matrix factorization-based recommendations.

7. Time Series Analysis:

• Forecasting: Work on time series forecasting problems, such as predicting stock prices, weather data, or sales forecasts.

8. Transfer Learning:

• Pretrained Models: Fine-tune pretrained models like BERT or GPT for specific NLP tasks.

9. Healthcare and Medical Imaging:

• Explore applications of machine learning in medical diagnosis, disease detection, or medical image analysis.

10. Anomaly Detection:

• Develop models for anomaly detection in various domains like cybersecurity, fraud detection, or industrial equipment monitoring.

Remember to start with beginner-friendly resources such as online courses, tutorials, and small-scale projects. As you gain more confidence and experience, you can delve into more complex topics and research areas. Additionally, consider seeking guidance from professors or mentors who specialize in machine learning to assist you in your research journey.