Help me out with my Machine Learning Project

Question

I am a final year Cse student and I seek experts guidelines on the subject.
I need some guidelines about the domain and a few examples of real world ML projects. Anyone willing to guide me in this? Suggest me some problems as well if possible. Thanks

Answer 1

I would suggest starting from the basics about what machine learning is as a discipline and what are the typical problems that can be solved using machine learning algorithms.
https://www.sololearn.com/learn/714/?ref=app

Real world projects are in abundance and some of them are publicly available for solving (often for real world money, too ;) -- for example at www.kaggle.com
Anyway, start from the basics and if you need any assistance or guidance, write some more inquiries.

For a final project at CS studies I've seen image recognition/computer vision, e-commerce product recommendation systems and chatbots, for example. Seem quite complete as projects and not too exhausting, at least to a 'working' level solution.

Answer 2

from __future__ import print_function, division
from terminaltables import AsciiTable
import numpy as np
import progressbar
from mlfromscratch.utils import batch_iterator
from mlfromscratch.utils.misc import bar_widgets

class NeuralNetwork():
"""Neural Network. Deep Learning base model.
Parameters:
-----------
optimizer: class
The weight optimizer that will be used to tune the weights in order of minimizing
the loss.
loss: class
Loss function used to measure the model's performance. SquareLoss or CrossEntropy.
validation: tuple
A tuple containing validation data and labels (X, y)
"""
def __init__(self, optimizer, loss, validation_data=None):
self.optimizer = optimizer
self.layers = []
self.errors = {"training": [], "validation": []}
self.loss_function = loss()
self.progressbar = progressbar.ProgressBar(widgets=bar_widgets)

self.val_set = None
if validation_data:
X, y = validat

Answer 3

Absolutely! As a final-year CSE student, you're at the perfect stage to dive into machine learning (ML) projects that not only strengthen your resume but also deepen your practical understanding of algorithms and data.

🔍 Guidelines for Choosing a Machine Learning Domain:
Interest Area: Choose a field that genuinely interests you—like healthcare, agriculture, education, or cybersecurity.

Data Availability: Ensure that open-source or real-world datasets are available for your topic.

Scalability: Pick a project that can start simple but offers room to scale (e.g., adding deep learning later).

Tech Stack: Learn tools like Python, scikit-learn, TensorFlow, or PyTorch early.

💡 Real-World ML Project Ideas:
Fake News Detection – Using NLP and classification algorithms.

Customer Churn Prediction – Predicting which users are likely to leave a service.

Crop Disease Detection – Using image classification for leaf disease identification.

Resume Ranker – ML model to screen resumes based on job descriptions.

Student Performance Predictor – Predict results using attendance, study hours, etc.

💡 Problems You Can Solve:
How can we predict diseases early using health records?

Can traffic patterns be predicted to reduce congestion?

Can ML personalize e-learning platforms based on a student’s ability?

For a detailed list of machine learning project ideas tailored for final-year CSE students, you can check out this excellent resource:
👉 Final Year Projects for CSE – ML Ideas & Guidance
https://www.ieeexpert.com/final-year-projects-for-cse/

It covers step-by-step project ideas, tools to use, and video guidance for implementation.

Feel free to ask if you need help choosing the right one based on your skill level!