Practice these ML portfolio projects to get you hired instantly in your dream tech job
Practice with real-world projects is the best approach to learning and perfecting the art of machine learning. You can build a solid foundation in various machine learning algorithms and improve your résumé by working on ML portfolio projects. However, the proverb states that every journey begins with a single step.
Getting a good job in the field of Machine Learning is getting very competitive. The best way to showcase your Machine Learning skills is in the form of machine learning projects. Good ML projects show that you can apply those Machine Learning skills in your work. You may better understand the various machine learning ideas with the help of machine learning projects. Each project investigates novel machine learning techniques, data sets, and commercial issues. By using all these machine learning tasks, you will develop a solid foundation in machine learning and related methods. The top ML portfolio projects are classified.
Here are 10 ML Portfolio Projects which will boost your resume and will help you to get a job:
1. Movielens Dataset recommendations for movies
Nowadays, almost everyone streams movies and TV shows using technology. While deciding what to watch next can be difficult, suggestions are frequently given based on a viewer’s past viewing habits and personal preferences. Machine learning is used to accomplish this, making it a simple and enjoyable project for novices. Using data from the Movielens Dataset and either the Python or R programming languages, novice programmers can practice their skills. Movielens presently has more than 1 million movie ratings for 3,900 films that were created by more than 6,000 users.
2. TensorFlow
The open-source artificial intelligence library is a great resource for learning machine learning for beginners. They can utilize TensorFlow to build Java projects, data flow graphs, and a variety of other applications. It also contains Java APIs.
3. Walmart’s sales forecasting
Businesses can come close to machine learning even though it may not be possible to predict future sales precisely. Using Walmart as an example, developers can obtain data on weekly sales by locations and departments for 98 products across 45 stores. Making better data-driven judgments for channel optimization and inventory planning is the aim of a project of this scale.
4. Predictions of Stock Prices
The same data sets used for sales forecasting, volatility indices, and fundamental indicators are also used to make forecasts about stock prices. Beginners can start small with a project like this and make forecasts for the coming few months using stock-market datasets. It’s an excellent method to become comfortable making predictions with large datasets.
5. Smartphone Human Activity Recognition
Many modern mobile gadgets are built to recognize when we are performing a certain activity, like cycling or running, automatically. Machine learning is at work here. Novice machine learning engineers use a dataset with fitness activity records for a few people (the more, the better), which was gathered through mobile devices equipped with inertial sensors, to practice with this type of project. Then, students can create categorization models that can precisely forecast future actions. This may also aid in their comprehension of multi-classification puzzles.
6. Predictions of Wine Quality
It can be difficult to find wines that you like while you wine shopping. Unless you are a specialist who takes into consideration several criteria like age and price, there is no definite way to determine whether a wine is of good quality. The Wine Quality Data Set contains these specifics to assist in predicting quality, making it a pleasant machine learning experiment. This project gives ML newcomers practice with data exploration, data visualization, regression modeling, and R programming.
7. Breast Cancer Diagnosis
This machine learning experiment makes use of a dataset that can predict whether a breast tumor is likely to be malignant or benign. The thickness of the lump, the proportion of naked nuclei, and mitosis are among the variables considered. This is also an excellent way of learning ML.
8. Classification of Iris
One of the most well-known, earliest, and easiest machine learning tasks for beginners is the Iris Flowers dataset. Learners must master the fundamentals of handling numerical quantities and data as part of this assignment. The length and width of the sepals and petals are among the data points. A project that successfully sorted irises into one of three species using machine learning.
9. Twitter Sorting Specific Tweets
It would be wonderful to rapidly filter tweets that contain particular words and information. Fortunately, there is a beginner-level machine learning project that enables programmers to develop an algorithm that uses scraped tweets and a natural language processor to identify which were more probable.
10. Creating Digital Versions of Handwritten Documents
Deep learning and neural networks, two machine learning components crucial for image recognition, are ideal for practice in this kind of project. Beginners can also learn how to use MNIST datasets, logistic regression, and how convert pixel data into images.
Source: analyticsinsight.net
