Dear Md Safayet Al Hossain ,

Well, If you’re stepping into machine learning research, I’d recommend starting with supervised learning, particularly classification and regression models. They’re fundamental and will help you understand core concepts like loss functions, optimisation algorithms (like gradient descent), and overfitting—all essential for more complex models down the line. Plus, there’s no shortage of high-quality datasets and literature, so you’ll have a solid knowledge base to work with.

From there, diving into NLP or computer vision might be worth considering. NLP is especially interesting right now with transformer models like BERT and GPT, which are heavily used in the industry. Pre-trained models from Hugging Face can speed up the learning curve, letting you experiment with state-of-the-art architecture without starting from scratch. CV is similarly accessible, and frameworks like TensorFlow and PyTorch have plenty of pre-trained models for image classification, object detection, and segmentation.

If you're drawn to something more experimental, exploring reinforcement learning could be rewarding, especially if you’re interested in developing agents or simulations. While deep reinforcement learning can be complex, starting with simpler environments (such as OpenAI Gym) can give you hands-on insight into policy optimisation and Q-learning, which are both widely applicable across disciplines.

Lastly, I’d emphasise learning model evaluation and tuning techniques—grid search, random search, cross-validation, etc. As models get more complex, optimising hyperparameters and understanding metrics like AUC, F1-score, and ROC curves become crucial for robust performance assessment. Mastering these aspects early on will set you apart, especially when you start deploying models or working with imbalanced datasets.

Any of these areas are solid for building up advanced understanding, and they’ll give you practical skills that can scale as you dive deeper.

You may want to consider the following references:

https://books.google.com.bn/books?hl=en&lr=lang_en&id=0xPdEAAAQBAJ&oi=fnd&pg=PP32&dq=machine+learning+for+a+beginner+researcher%3F&ots=mxnQ6N6qk1&sig=6i9l2MbN3cxj23aU9hdRjtqQLys&redir_esc=y#v=onepage&q=machine%20learning%20for%20a%20beginner%20researcher%3F&f=false

https://books.google.com.bn/books?hl=en&lr=lang_en&id=8-fFEAAAQBAJ&oi=fnd&pg=PA3&dq=machine+learning+for+a+beginner+researcher%3F&ots=NSmWRlWCWt&sig=A8KW5Rfh_IeWWQ7lLGXlF9lv2hM&redir_esc=y#v=onepage&q=machine%20learning%20for%20a%20beginner%20researcher%3F&f=false

https://books.google.com.bn/books?hl=en&lr=lang_en&id=1-4lDQAAQBAJ&oi=fnd&pg=PP1&dq=machine+learning+for+a+beginner+researcher%3F&ots=29jMJJNM__&sig=tVFjh8I7LAYMT7VWiFCYnI1sk_g&redir_esc=y#v=onepage&q=machine%20learning%20for%20a%20beginner%20researcher%3F&f=false

https://books.google.com.bn/books?hl=en&lr=lang_en&id=V5KvEAAAQBAJ&oi=fnd&pg=PA1&dq=machine+learning+for+a+beginner+researcher%3F&ots=UQIsgWenY1&sig=UTBRzGSmjH1u0KxjC93no9l6blY&redir_esc=y#v=onepage&q=machine%20learning%20for%20a%20beginner%20researcher%3F&f=false

https://books.google.com.bn/books?hl=en&lr=lang_en&id=iNuSDwAAQBAJ&oi=fnd&pg=PP1&dq=machine+learning+for+a+beginner+researcher%3F&ots=O94PwcaFR2&sig=9hosiWh9X6-zbjadgpD7xVNTcVw&redir_esc=y#v=onepage&q&f=false

Thank you, sir.

Hi Md Safayet AI Hossain,

Let me suggest this paper, it may help in your research: Article Hybrid approaches to optimization and machine learning metho...

Dear Mr. Al Hossain

If you're just starting out in machine learning, here are a few research topics you might find interesting:

Thank you, sir, for your suggestion. It is helpful to understand more easily.