The proverb says that every journey starts with a single step. The most excellent way to learn and master the skill of machine learning is through practice with real-world projects. By working on different ML portfolio projects, you can strengthen your knowledge of different machine learning algorithms and enhance your resume.

It is becoming increasingly difficult to get employment in machine learning. Machine learning projects are a great way to show off your Machine Learning skills. Good ML projects demonstrate your ability to use those skills in the workplace. Machine learning projects can help you comprehend the various machine learning concepts in a better way. Also, each project examines new machine learning techniques, data sets, as well as commercial problems. Using these entire machine learning activities, you will build a strong foundation in machine learning and associated techniques.

This article will discuss the 10 best machine learning portfolio projects that will help to improve your resume in a highly competitive job market.

Let’s get started!

#1. Using A Smartphone To Recognize Human Activity

Several modern mobile devices are designed to automatically detect when we perform specific activities such as running or cycling. Machine learning comes into play here. Machine learning beginners practice with this kind of project using a dataset comprising fitness activity records for some individuals, which was collected via mobile gadgets equipped with inertial sensors. Students can then develop classification models that can precisely predict future actions. This might also help them understand multi-classification puzzles.

#2. Classification Of Iris

The Iris Flowers dataset is one of the easiest, most popular, and earliest machine learning activities for beginners. This assignment requires that learners are proficient in handling numerical data and quantities. Data points include the width and length of the petals and sepals. A project that correctly classifies irises into one of three types using machine learning.

#3. Walmart’s Sales Projections

Machine learning can be applied to businesses, although it is impossible to anticipate future sales accurately. Using Walmart as an instance, developers may get information on weekly sales broken down by departments & locations for 98 products across 45 Walmart shops. A project of this size aims to improve data-driven decisions for channel optimization & inventory planning.

#4. Diagnosis of Breast Cancer

The machine learning experiment uses a dataset to predict whether breast cancer is likely to become malignant or benign. Some important factors taken into account are mitosis, the percentage of naked nuclei, and the lump’s thickness. This is an excellent method of learning the concept of machine learning.

#5. Movielens Recommendations Of Datasets For Films

Almost everybody streams TV shows and films using technology these days. It can be challenging to decide what to watch next; however, recommendations are frequently made based on a viewer’s past viewing patterns and personal preferences. This is accomplished using machine learning, which makes it an easy and interesting project for beginners. Novice engineers can practice their skills using information from the Movielens Dataset and either R programming or Python languages. Currently, Movielens has over 1 million film ratings for 3,900 movies, which more than 6,000 users created.

#6. Stock Price Predictions

Stock price predictions are made using the same data sets used for volatility indices, fundamental indicators, and sales forecasting. A project like this can be a good starting point for beginners. They can then make predictions about the next few months using the stock-market datasets. It is a great way to practice making predictions with huge datasets.

#7. Sorting Particular Tweets on Twitter

Quickly filtering tweets for specific words and content would be fantastic. An easy-to-use machine learning project for beginners allows programmers to create an algorithm that uses a natural language and scraped tweets processor to determine which were highly probable.

#8. Wine Quality Predictions

While wine shopping, finding the wines you enjoy might be challenging. There is no precise way to judge whether a wine is of good quality unless you are a specialist who considers various factors, including the price and age of the wine bottle. These details are included in the ‘Wine Quality Data Set’ to help with quality prediction, making it an enjoyable machine learning experiment. This project allows machine learning novices to practice data visualization, data exploration, R programming, and regression modeling.

#9. Creating Digital Versions Of Hand-Written Documents

Neural networks and deep learning are the best machine learning tools for picture recognition. Also, it is good for practicing such a type of project. Additionally, beginners can learn how to apply logistic regression and MNIST datasets, and turn pixel data into pictures.

#10. TensorFlow

A fantastic resource for learning ML for beginners is the open-source artificial intelligence (AI) library. They can use TensorFlow to make data flow graphs, various Java projects, as well as a wide range of other apps. It also includes Java APIs.

Conclusion

These top 10 machine learning portfolio projects mentioned in this article are great ways to make your resume stand out and help you land the job you have always wanted. They are all unique and will show potential employers that you have the skills they are looking for. With a little bit of effort and dedication, you can easily create a portfolio that will impress any potential employer. So, if you want to make your resume stand out, start working on one of these projects. So what are you waiting for? Get started on a project today!

Thanks for reading!

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by Nicholas Stovold