What are some really interesting machine learning projects for beginners?

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#1

What are some really interesting machine learning projects for beginners?

#2

There are some projects we worked during our machine learning course.

A structured workflow for model building is followed and you will be able to practice:
• Importing data
• Cleaning data
• Splitting it into train/test or cross-validation sets
• Pre-processing
• Transformations
• Feature engineering

Machine Learning Projects for beginners:

  1. Loan Prediction using Machine Learning: build a model to predict how much a user is going to take loan and classify them according it.
    Dataset link: https://www.kaggle.com/altruistdelhite04/loan-prediction-problem-dataset

  2. Housing Prices Prediction Project: Predict house prices of areas. https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

  3. Titanic Survival Project: predict the survival of people in titanic ship i.e. they will survive or not.
    Dataset link: https://www.kaggle.com/c/titanic

  4. Wine Quality Test Project: Here we predict the quality of wine
    Dataset link: https://archive.ics.uci.edu/ml/datasets/wine+quality

There are machine learning hands- on project provided by CloudXLab with their Machine learning and Machine learning specialization course


Projects like:

  1. Analyze Emails
    Churn the mail activity from various individuals in an open source project development team.

  2. Predict the median housing prices in California
    We start Machine Learning course with this end-to-end project. Learn various data manipulation, visualization and cleaning techniques using various libraries of Python like Pandas, Scikit-Learn and Matplotlib.

  3. Classify handwritten digits in MNIST dataset
    The MNIST dataset is considered as “Hello World!” of Machine Learning. Write your first classification logic. Starting with Binary Classification learn Multiclass, Multilabel, Multi-output classification and different error analysis techniques.

  4. Noise removal from the images
    Build a model that takes a noisy image as an input and outputs the clean image.

  5. Predict bikes rental demand
    Build a model to predict the bikes demand given the past data.

  6. Build a spam classifier
    Build a model to classify email as spam or ham. First, download examples of spam and ham from Apache Spam Assassin’s public datasets and then train a model to classify email.

  7. Build cats classifier using neural network
    In this project, you will build a basic neural network to classify if a given image is of cat or not.

You can also refer to CloudXLab Blog ( https://cloudxlab.com/blog/) for more Machine learning real like usage. Such as: How to build a Number Plate Reader, How to run object detection on CCTV etc.

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