1. Movie Recommendations with Movielens Dataset

Almost everyone today uses technology to stream movies and television shows. While figuring out what to stream next can be daunting, recommendations are often made based on a viewer’s history and preferences. This is done through machine learning and can be a fun and easy project for beginners to take on. New programmers can practice by coding in either Python or R languages and with data from the . Generated by more than 6,000 users, Movielens currently includes more than 1 million movie ratings of 3,900 films.

2. TensorFlow

This open-source artificial intelligence library is an excellent place for beginners to improve their machine learning  they can use the library to create data flow graphs, projects using Java, and an array of applications. It also includes APIs for Java.

3. Sales Forecasting with Walmart

While predicting future sales accurately may not be possible, businesses can come close to machine learning. For example, Walmart provides datasets for 98 products across 45 outlets so developers can access information on weekly sales by locations and departments. The goal with a project of this scope is to make better data-driven decisions in channel optimization and inventory planning.

4.Stock Price Predictions

Similar to sales forecasting, stock price predictions are based on datasets from past prices, volatility indices, and fundamental indicators. Beginners can start small with a project like this and use stock-market datasets to create predictions over the next few months. It's a great way to become familiar with creating predictions based on massive datasets. To get started, download a stock market dataset from Quantopian or Quandl.

5.Human Activity Recognition with Smartphones

Many of today's mobile devices are designed to automatically detect when we are engaging in a specific activity, such as running or cycling. This is machine learning at work. To practice with this type of project, novice machine learning engineers use a dataset that contains fitness activity records for a few people (the more, the better) that was collected through mobile devices equipped with inertial sensors. Learners can then build classification models that will accurately predict future activities. This can also help them understand how to solve multi-classification problems.

6.Wine Quality Predictions

Shopping for new and unfamiliar wines can be a hit or miss affair. There’s no surefire way to know whether a wine is of high quality unless you are an expert who takes into account different factors like age and price. The Wine Quality Data Set can be a fun machine learning project that contains such details to help predict quality. Through this project, ML beginners get experience with data visualization, data exploration, regression models, and R programming.

7.Breast Cancer Prediction

This machine learning project uses a dataset that can help determine the likelihood that a breast tumor is malignant or benign. Various factors are taken into consideration, including the lump's thickness, number of bare nuclei, and mitosis. This is also an excellent way for new machine learning professionals to practice R programming.

8.Iris Classification

The Iris Flowers dataset is a very well known and one of the oldest and simplest for machine learning projects for beginners to learn. With this project, learners have to figure out the basics of handling numeric values and data. Data points include the size of sepals and petals by length and width. Using machine learning, a successful project classified irises into one of three species.

9. Sorting of Specific Tweets on Twitter

In a perfect world, it would be great to filter tweets containing specific words and information quickly. Luckily, there's a beginner-level machine learning project that lets programmers create an algorithm that takes scraped tweets that have been run through a natural language processor to determine which were more likely to match specific themes, talk about certain individuals, and so on.

10.Turning Handwritten Documents into Digitized Versions

This type of project is a perfect way to practice deep learning and neural networks — essentials for image recognition in machine learning. Beginners can also learn how to turn pixel data into images, as well as how to use logistic regression and MNIST datasets.

How do I start my own machine learning project?

• Search for a problem that you can solve.
• Find suitable data and refine the question.
• Import the data from formats, like JSON, XML, CSV, etc., based on your analysis.
• Explore and clean the data by removing any null and/or nonsensical values, etc.
• Develop and refine the model.