What are some basic projects in machine learning?

Machine learning project ideas will assist you in understanding all of the practical aspects of your profession and making you employable in the field.

Machine learning project :-

  • Music Recommendation System Project
  • Iris Flowers Classification ML Project
  • Stock Prices Predictor using TimeSeries
  • Predicting Wine Quality using Wine Quality Dataset
  • House Pricing Prediction Project

Only through much practice and experimentation can one become a master of machine learning.

Check out this blog for more understanding about the 6 Interesting Machine Learning Project Ideas For Beginners

  • 1| Sentiment Analysis of Product Reviews.
  • 2| Stock Prices Prediction.
  • 3| Sales Forecasting.
  • 4| Movie Ticket Pricing Prediction.
  • 5| Music Recommendation.
  • 6| Handwritten Digit Classification.
  • 7| Fake News Detection.

Machine Learning has become a boom lately, everyone is doing it, everyone’s learning it and implementing it. Although there are many things which still need to be cleared in terms of concepts and approach.
Not every problem which has numbers involved in it is a machine learning problem. There’s a great saying, if the only tool you have is a hammer, you tend to see every problem as a nail.

  1. Learning from the data is required.
  2. Prediction of an outcome is asked for.
  3. Automation is involved.
  4. Understanding the pattern is required like that in the case of user sentiments.
  5. Same as point d for building recommendation systems.
  6. Identification/Detection of an entity/object is required.
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Some fundamental machine learning problems for beginners are as follows:

Classification: Data is labeled, which means it is assigned a class, such as spam/non-spam or fraud/non-fraud.

The decision under scrutiny is whether or not to assign labels to previously unlabeled data.

This is a discriminating issue because it models the differences and similarities across groups.

Regression: Instead of a label, data is labeled with an accurate value (imagine floating point).

Time bound data, such as the price of a stock over time, is a straightforward example to comprehend. The choice being modeled is what value to estimate for new uncertain data.

Clustering: Although the data is not labeled, it can be split into groups based on similarity and other natural structure characteristics.

Organizing photographs by faces without names, where the human user must supply names to groups, is an example from the above list, as is iPhoto on the Mac.

Extraction of propositional rules: Data is used to extract propositional rules (antecedent/consequent aka if-then).

Such rules may or may not be directed, implying that the methods find statistically supportable correlations between data attributes without necessarily incorporating something that is being predicted.

One example is discovering a link between the buying of alcohol and the purchase of diapers.