Top 11 Machine Learning Projects For Beginners: The concept of machine learning has revolutionized the market. We have seen several developments taking place in this domain. The idea is to create machines that think and behave like humans. The concept of machine learning is not new. However, it’s only recently that it has picked up the pace. Big data analytics, data science, and allied technologies have catalyzed the growth of concepts like machine learning.
This blog takes you through some of the important developments in machine learning. We’ll Discuss the top 11 machine-learning projects for beginners. But before leaping, let’s have a closer look at the recent development in this field.
Noteworthy developments in the domain of Machine Learning
- The global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029 at a CAGR of 38.8%
- There has been considerable development of machine learning in healthcare. A recent example is the COVID-19 pandemic, wherein the Massachusetts Institute of Technology researched the data from COVID-19 patients and the efficacy of the quarantine.
- The impact of artificial intelligence and machine learning is also increasing in the marketing sector. According to Forbes, around 57% of executives believe that AI and ML help enhance customer satisfaction.
- The e-commerce industry is yet another business domain benefiting from machine learning. With the help of these algorithms, it becomes easier for the e-commerce industries and retailers to understand their customer’s preferences and buying patterns. Consequently, it helps companies offer personalized services.
The Growing significance of Machine Learning
We are creating more data than ever. The limitless quantity of available information and data storage has propelled the growth of machine learning. Many industries are now adopting this robust model to analyze complex data and hidden details. This data and the information help organizations build customer-centric strategies.
There are several other developments that have taken place in the field of machine learning. Irrespective of the business size, machine learning can be beneficial, and so every business can benefit from it.
Why Machine Learning Is Important for Businesses?
Growing market complexities and behavioral patterns of the customer have made it important for organizations to adopt tools that can help them assess this behavior. This helps the organization offer services and products which can help the customer. Eventually, it helps them formulate strategies focusing on customers’ needs and preferences. The following section of the blog unfolds the new projects under the machine learning realm.
The growing significance of machine learning in different industries also highlights the need for skilled professionals. Hence, there has been a considerable rise in the demand for machine learning experts. So, an individual willing to explore a prosperous and promising career opportunity can explore machine learning courses.
Joining the machine learning program gives you access to a plethora of tools. However, as a beginner, you must also learn to use these tools practically. Hence, we have compiled a list of 11 projects. Working on such projects will help you test your skills and build a strong portfolio for job recommendations.
Top 11 Machine Learning Projects:
Let’s start with the top 11 machine learning projects for beginners that are being used by machine learning engineers.
1. Movie Recommendations with Movielens Dataset –
We all love to watch films, but sometimes, finding the right movie can be challenging. Hence, you can work on this need and pick up a project on movie recommendations.
You would have often noticed this feature while surfing on an e-commerce website. The user is often shown recommended products. The same is applicable in the case of a movie or a music website.
Let’s understand how this works. While working on this project, one can use data sets from Asia’s leading streaming services to build the movie recommendation system.
The primary work here is to predict the chances of a user listening to a song repeating actively within a given time frame. One also tries to determine which new movie or artist the user might like based on previous searches or choices. To make a precise decision, you can use classification machine learning algorithms. You can also use deep learning algorithms like a neural network.
2. TensorFlow
This is an open-source library that is accessible to all. Using Java and several other applications, machine learning students can use this library to create graphs and projects.
3. Sales Forecasting with Walmart –
Sales forecasting is important for every organization. Knowing the customers and planning the right strategy can help them generate more profit regardless of the business size.
Many companies like Walmart are using machine learning for sales forecasting. This helps them understand the sales pattern and the factors that can help them convert the leads into clients. With the help of machine learning tools, we can create an accurate sales forecasting model.
For example, a salesperson can use predictive analytics to forecast potential customers’ behavior. The Auto-Regressive Integrated Moving Average (ARIMA) model is helpful here. With this algorithm, we can determine the cause behind the data. Based on it, we can create predictions. This model also uses exponential smoothing, which is based on previous data.
Some of the common methods used for sales forecasting are our multivariate model and regression analysis. The beginners can use the data set provided by Walmart for the sales forecasting project. Walmart provides data sets for 98 products from its 45 outlets. The developers can understand the weekly sales by location and department. This will eventually help in data-driven decision channel optimization.
4. Stock Price Predictions
The stock market is highly vulnerable. Market fluctuation and changing prices can impact the value of the stock. A seasoned trader would always want to keep a tab on the changing value. Hence stock price prediction can be helpful. So, this is an important area where one can find the application of machine learning.
If you are pursuing a machine learning course, stock market prediction can be an interesting machine learning project idea. With the stock price predictor system, one can learn about the company’s stocks.
The biggest challenge in working with the stock market data is to predict the prices. This system is useful for traders and brokers actively investing in the market. The system assesses a company’s performance and its future.
One has to assess different data like prices, global macroeconomic indicators, fundamental indicators, and others. To begin your project on working with stock market data, you can choose a simple machine learning problem like predicting six months’ price movement based on fundamental indicators.
You can download stock market data sets from Quandl.com. And to understand the performance of the market, you can use time series analysis.
5. Human Activity Recognition with Smartphones
We all use smartphones. They have penetrated our lives. But did you know that these smartphones store a lot of data about us? This data can be helpful in medical research.
Human activity recognition(HAR) has wider applications in the healthcare system and medical research. Machine learning engineers can use this data to derive useful insights. As a machine learning engineer, when you start working on the project of assessing human activity behavior, you can design a robust activity recognition system based on a smartphone.
The system can use a 3-D smartphone accelerometer as a sensor to collect time-series signals. With the data derived from this, one can classify and recognize the human activity.
6. Wine Quality Predictions
As a machine learning student, if you are looking for an impressive project, you can consider wine quality prediction. Several factors impact the quality and taste of the wine. Several parameters go into wine quality certification; this includes alcohol quantity, volatile acidity, pH level, fixed acidity, and others. Now all these are data sets or information that eventually impacts the quality of the wine.
As a machine learning student, your project is to build a model that predicts the quality of wine by assessing the different parameters. The quality data set consists of different observations with 11 independent and one dependent variable. One can use data visualization techniques to find out the feature of another variable section that also serves as an input to the machine learning algorithm. Based on this information, they prepare the report.
As a part of this project, students will use regression models, data exploration, data visualization, and R programming.
7. Breast Cancer Prediction
– As we have mentioned in the beginning, Machine learning has significantly penetrated the healthcare sector. It was widely used during the pandemic to assess Covid infected patients’ symptoms. A similar application of data is also possible for breast cancer prediction.
There are several causes of breast cancer. As a part of the machine learning project, one assesses the different parameters that can result in an increased probability of a person getting affected by breast cancer.
A part of this project also includes the study of whether the tumor is benign or malignant. The user builds a classifier, which is used to train on 80% of a breast cancer histology image dataset.
8. Classification of the Iris flower under different species
– It is one of the simplest and oldest machine-learning projects. In this part of the project, the learner studies different data points. It includes the size of the petals and sepals their length and width. Using machine learning, one can create a successful algorithm to classify the Iris species based on the data sets.
One can download the Iris flower data set from UCI ML Repository. The objective of this project is to classify iris flowers under three species:
- Virginia
- Setosa
- Versicolor
The distinction is based on the length and width of petals and sepals. One can also add this project to the deep learning project portfolio using advanced algorithms.
9. Sorting of Specific Tweets on Twitter
– You can also call it sentiment analysis. As the access to the Internet increases, people actively use social media platforms to share their opinion. This has prepared a new age of growth. Millions of opinions from different parts of the world are visible on social media forums. These posts have the potential to impact the thought process and mindsets of other individuals as well.
The primary objective of using platforms like Twitter is to reach the maximum audience. One can effectively analyze the tweets and valuable information to formulate their tweets. Storing these opinions in a structured manner and using them to analyze the public reaction can be helpful for businesses. Whether you are an author or running a business, understanding people’s sentiments will help you formulate strategies and ideas that will directly impact your target audience.
The objective of using platforms like a tweet is to gain maximum support from the audience. This is only possible when one can put forward ideas that have the power to influence people. With the help of a machine learning algorithm, one can assess and analyze the tweets that majorly impact people. Based on it, they can create their strategy and present it to their target audience.
10. Assessing social media activity-
An extension of the above-mentioned project could be the assessment of the activity of social media followers. Understanding this activity is empirical for content creators and corporate firms. To understand what increases or decreases the number of followers, one needs to use the data set that will give them the results of the type of activities of people on the social media platform. This information is not directly accessible from platforms like Facebook or Instagram; hence, one has to explore other sources of data sets.
11. Image recognition
– This can be a great project for machine learning beginners.. Manually transcribing handwritten data can be challenging. And it can also lead to errors. Using automated handwriting recognition, one can cut down on time and increase productivity without compromising on the quality of work.
Handwritten documents can be an input for the future applications of machine learning. It is a wider field encompassing artificial intelligence, pattern recognition, and computer vision.
The algorithmic platform performs handwriting recognition. As a part, it detects characteristics from pictures and touchscreen and converts them into a machine-readable form. This becomes the data set for the machine learning algorithms.
There are different approaches for online and offline hand recognition. It includes statistical methods, neural networks, and syntactic methods. Some systems also recognize strokes, while others apply recognition to single characters or words.
Additional ideas for machine learning projects
There is no shortage of options for machine learning projects. An effective machine learning course will have a case study after the completion of the module. The objective is to assess and test the skills of the learner. Hence, it will give them a better understanding of their learning status.
We have discussed some of the projects above. However, the project idea is not limited to the options mentioned here. You can explore several other options. The following section highlights some other machine-learning project ideas for beginners:
- Creating emoji with python
- Housing price prediction project
- Fake news detection project
- Bitcoin price predictor
- Credit default in credit risk prediction
- Driver demand prediction
- Identifying plants species
- Profit Prediction model
These are just a few examples where one can put their machine learning knowledge into a practical field. In conclusion, machine learning finds applications in our daily life. And we cannot undermine its growing significance in the times to come. Consequently, it has led to a rise in the demand for skilled and qualified machine learning professionals.
What’s next?
To use this data precisely, one needs skilled professionals. Pickl prepares you for the best move ahead. As a part of the learning program, you also learn about the different projects and ideas on how to use machine learning programs.
Over the years, the reliance on data and its applications has seen a meteoric rise. Pickl provides one of the best learning platforms for machine learning, Python, and data science. All these fields are interrelated. The burgeoning demand for data science professionals and machine learning experts highlights its penetration in the different industrial domains. Irrespective of the industry size, companies can reap the benefits of data.
The bottom line
We hope this blog’s top 11 machine learning engineers for beginners are able to provide you with satisfactory knowledge. The future of machine learning looks promising. There is a growing significance of machine learning in the present time. Hence, it has emerged as a popular career opportunity for many. And so, many individuals are now inclined to enroll in machine learning courses. But not every course is suitable to provide you with knowledge and expertise. Hence it all boils down to choosing a course that gives you a conceptual understanding and practical expertise on machine learning concepts.
Hence, enrolling in the best machine learning course is paramount. In addition, one must also prepare a strong portfolio after the completion of the course. And so, working on live projects is a must. You can refer to the above-mentioned options. Furthermore, enrolling on a platform gives you the leverage to explore the application of your conceptual skills. Hence, if you are looking for promising career growth, invest in pursuing machine learning and data science. These are the technologies of the future and promise a prosperous future.
