Machine learning is a lot like it sounds: the idea that various forms of technology, including tablets and computers, can learn something based on programming and other data. It looks like a futuristic concept, but this level of technology is used by most people every day. Speech recognition is an excellent example of this. Virtual assistants like Siri and Alexa use the technology to recite reminders, answer questions, and follow commands.
As machine learning proliferates, more professionals are pursuing careers as machine learning engineers. One of the best ways to get started is by getting hands-on and developing a project, taking courses like AI & Machine Learning Bootcamp, and other many free resources online.
Top 10 Machine Learning Projects:
Here is the list of the top 10 simple machine learning projects that we will be learning in detail:
- Movie Recommendations with Movielens Dataset
- TensorFlow
- Sales Forecasting with Walmart
- Stock Price Predictions
- Human Activity Recognition with Smartphones
- Wine Quality Predictions
- Breast Cancer Prediction
- Iris Classification
- Sorting of Specific Tweets on Twitter
- Turning Handwritten Documents into Digitized Versions
Let's discuss each in detail:
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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 Movielens Dataset. 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 skills. With TensorFlow, 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.
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Professional Certificate Program in AI and MLExplore Program4. 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.
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Professional Certificate Program in AI and MLExplore Program6. 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.
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Professional Certificate Program in AI and MLExplore Program9. 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.
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FAQs
1. 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.
2. What are the best machine learning projects for the final year?
The best machine learning projects for the final year are the recommender system project, stock price prediction project, AI-driven sentiment analyzer, and sales forecasting project.
3. Is AI and machine learning the same?
AI is a much broader concept used to create intelligent machines capable of stimulating human thinking, behavior, and capability. Machine learning, on the contrary, is the subset or application of AI that enables machines to learn from data without being explicitly programmed.
4. Is machine learning hard?
While many of the advanced machine learning tools can seem hard to use and necessitate advanced knowledge in mathematics, statistics, and software engineering, the easily accessible basics can be availed by beginners to perform a lot of tasks.
5. Which language is best for machine learning?
Python is the best language for machine learning as it is easy to learn, scalable, and open source. It is used and prioritized by almost 69% of machine learning developers.
6. How can I learn AI and ML for free?
You can enroll in Simplilearn’s free courses in AI and machine learning. All the free courses offer resources, skill-based learning, and self-paced learning created by top practitioners.
7. Can I learn machine learning without coding?
Although you can learn a few machine learning tools without coding, pursuing a career in AI and machine learning will necessitate a little bit of coding.
Look at the video below that talks about Top 10 Machine Learning Projects that are majorly used in the industries. This video will also help any machine learning enthusiast to get an idea about how these projects are being implemented and what their benefits are.
8. How do I find machine learning projects?
Aspiring machine learning practitioners look for decent machine learning projects that they could enlist on their resumes. There are various sources for hunting ML projects that add breadth to your portfolio. Some of the most popular ones include ProjectPro and Kaggle. You can generate your own machine learning experience and enhance your chances of getting hired.
9. What are the three key steps in a machine learning project?
A machine learning project can be broken down into three major steps - collecting and preparing data, choosing a model and evaluating it, and optimizing the process and parameter tuning.
10. How do I start a machine learning project?
Search for a suitable problem that you can solve. Find relevant data and refine the question. Import the data from various formats, like JSON, XML, CSV, etc., based on your analysis. Explore and clean the data by removing any null values. Refine the model.
11. What is the most important part of a machine learning project?
Data preprocessing is the most important part of a machine learning project because this step helps in building more accurate machine learning models. Data scientists devote up to 80% of their time to data preprocessing for the best results.
12. What are some good machine learning projects?
Some of the best machine learning projects are - recommender system project, AI-driven sentiment analyzer, stock price prediction project, and sales forecasting project.
13. Are machine learning projects difficult?
Advanced machine learning projects can be hard as they necessitate in-depth knowledge of mathematics, statistics, algorithm optimization, and software engineering. The easily accessible basic projects are simpler and can be availed by beginners. Still, the meticulous attention to optimization and identification of algorithm inefficiencies makes them challenging.
14. Where can I do ML projects?
Some of the best places to get hands-on ML project experience are as follows- Wine Quality Predictions, TensorFlow, Stock Price Predictions, Sales Forecasting with Walmart, and Human Activity Recognition with Smartphones.
15. Why do so many ML projects fail?
The primary reason behind the failure of machine learning projects is the unpreparedness of companies. Most enterprises are ill-equipped to see the projects through. A majority find machine learning projects difficult to handle because they tend to underestimate the overall work that would go into training models properly.
