Before we get to all the simple Machine Learning projects for beginners, let’s understand what ML really is. Artificial Intelligence and Machine Learning are quite the buzzwords today, especially with the widespread implementation of Big Data solutions (although ML has been around for many years before Big Data).
Machine Learning is a branch, or one form of application, of Artificial Intelligence which enables computational systems to learn from iterations and improve their functioning without any manual intervention.
In all essence, as the word describes, it allows a machine to ‘learn’, and with the vast amounts of data available today, learning can happen fairly quickly!
90% of the world’s current data was generated in the last 2 years!
To understand the concept of Machine Learning better, we can consider the example of one of the oldest and simplest forms of Machine Learning in existence, an email spam filter.
An email provider’s (such as Google) spam filter picks up on keywords embedded within emails that are being marked as spam around the world, in order to automatically mark similar emails as spam for the same and other users around the world.
It learns from users’ behavior to pick on what is spam, and what is not, so the user does not have to do it manually.
Machine Learning projects are not just built to perform end tasks, but rather written to analyze data and statistical models to look for patterns in order to improve tasks.
Machine Learning allows systems to perform better and more efficiently over time. There are 8 different types of Machine Learning algorithms based on the data input and output type, approach, and results. They are:
2. Unsupervised Learning
3. Reinforcement Learning
4. Self-Learning
5. Feature Learning
6. Sparse Dictionary Learning
7. Anomaly Detection
8. Association Rules
In order to achieve Machine Learning, programmers create models which are fed initial data that allows the program to learn and process future predictions. The 5 main types of ML models are:
1. Artificial neural networks
2. Decision trees
3. Support vector machines
5. Genetic algorithms
Real-World Applications of Machine Learning
1. Search Engines
The reason search engines work well and are popular is that they are able to predict what your search might be, and can also predict what information or articles you might find useful.
All of this is made possible by Machine Learning algorithms, which allows search engines to analyze your past search data to learn your preferences. Search engine based machine learning projects with source code is a great place to start learning.
2. Photo Tagging
Have you ever wondered how Facebook is able to suggest a tag on a photo accurately? It is because of facial recognition software and Machine Learning that allows Facebook to identify individuals in a photo.
3. Spam Detection
As described in the example earlier, email spam filters are capable of learning by keywords and senders what emails are likely to be spam.
4. Data Mining
Machine Learning helps improve automation processes by mining data (such as click data to improve UX) and analyzing it. Another incredible example where machine Learning helps automated processes is in the case of autonomous cars.
5. Predictive Analytics
Companies use Machine Learning programs that analyze past data and provide predictive analytics based on which decisions can be made. A shopping market, for example, can get product sales predictions to adjust their inventory accordingly.
Benefits of Machine Learning to Businesses
1. Helps Businesses Make Accurate Sales Forecasts
Machine learning programs are capable of consuming massive amounts of data, analyzing them, and providing predictive information that organizations use to forecast sales and revenues. Companies are continually investing in projects on Machine Learning designed for predictive analytics.
2. Helps Make Accurate Predictions to Diagnose Patients Better & Prevent Calamities
Machine Learning enabled programs to help healthcare facilities to predict incidents related to high-risk patients, readmissions, etc. They also assist in better diagnosis and prescriptions. This is achieved by studying the past data on patient diagnosis, prognosis, and outcome.
3. Is Very Beneficial is the Finance Sector
Machine Learning is being used to improve trading portfolios and predict market movement by studying past factors and market results.
4. Improves Productivity in the Manufacturing Sector
Machine Learning programs are used to monitor equipment and predict breakdown, allowing manufacturing companies to take proactive action in order to mitigate downtime.
Why Should You Learn Machine Learning and Implement Machine Learning Projects?
The importance and vast use of Machine Learning are quite obvious today. As a programmer with the knowledge and skills to implement ML, you will have greater job opportunities for higher pays.
With the increasing use of Big Data in large companies, Machine Learning is a must in every programmer’s portfolio.
A Machine Learning engineer essentially builts programs or algorithms using programming languages like Python, Java, and Scala that are capable of ‘learning’ and evolving through data study. Some high paying job roles that will open up to you are:
1. Machine Learning Engineer
2. AI Engineer
3. Data Scientist
4. NLP Scientist
5. Business Intelligence Developer
6. Human-centred Machine Learning Designer
7. Business Intelligence (BI) Developer
These roles require the knowledge of programming languages mentioned earlier, and technologies like SQL, Hadoop, Hive, Spark, etc. depending on the role. The requirement of multiple skills is one important reason why we stress on learning via simple machine learning projects for beginners.
While one reason for learning Machine Learning is the high demand, another is the pay. According to Payscale, a Machine Learning engineer on an average earns $111,312 per annum, with a range of $75k – $153k per annum.
Getting started with Machine Learning requires learning the concepts of ML and mastering at least one programming language with supporting technologies.
Once you have acquired the knowledge, working on projects is the best way to get better at ML to crack interviews. To help in this process, we have listed below the top 11 simple Machine Learning projects with source code to help you improve.
11 In-Depth Machine Learning Projects for Beginners
1. Sports Predictor
If you have watched the movie MoneyBall, you would have seen the human form of machine learning in action. In the book (and movie), Oakland Athletics baseball team manager analysis team data to put in place a competitive team despite low budgets.
The idea of this first project is similar, to build a Machine Learning enabled program capable of analyzing sports data for different outcomes, which can be for the following machine learning projects:
(a) Betting – to predict scores and make the right bet.
(b) Talent Scouting– to analyze college-level sports data to predict career stats and path.
(c) Team Management – to build a formidable team-based predictive statistics.
2. Stock Market Movement Predictor
The stock market is a rich ocean of data and the perfect experimental domain for Machine Learning. There are numerous fields of data you can select to work with: price fluctuation, volatility indices, sentiment affects, etc., and each of these different fields already contains large pools of existing data. The availability of data at a granular level makes this an interesting project to execute.
You can select simple machine learning projects for beginners, such as the following:
(a) Company level stock price predictor – A program that predicts a company’s price fluctuation for the next 6 months based on past data and quarterly reports.
(b) Error Forecasting – Build a model that studies predicted and actual price volatility to improve predictions, or predict anomalies.
(c) Arbitrage Opportunities – Analyse data of different stocks to group them based on similar price movements, to predict similar results.
As a caveat, do not actually invest real money based on your projects. These are just for study purposes.
The availability of large data sets makes this one the best projects on machine learning.
3. Analyze Social Media Sentiment Using Twitter Dataset
Social Media platforms are a massive array of data that can be mined and analyzed to predict outcomes and make accurate conclusions. One such prediction is social media sentiment. You can analyze different tweets around one topic or keyword to predict the current mood of masses towards that topic.
You can use the Twitter dataset to gather tweets and metadata like hashtags, retweets, locations, users, etc. for analysis. As a beginner project, you can start by classifying a topic into different sentiments: happy, sad, angry, etc. You can build on the same project to predict sentiment for a topic that is just beginning to trend.
4. BigMart Sales Predictor
You can acquire BigMart’s sales data set online (you can get a similar data set for Walmart and other companies to create similar projects on machine learning with them) that consists of data for over a thousand products from over 10 different outlets in different cities.
You can work on a beginner project to build a program that uses regression models to predict the sale of each product across each of the outlets based on historical data that is available.
Such predictive analytics are very much in demand as they help stores like BigMart control their inventory to prevent both shortage and wastage.
5. Healthcare Improvement Program
Machine learning is playing a big role in the healthcare industry today. Programs that help with predictive diagnosis, patient condition monitoring, proactive alerts, etc. are helping healthcare professionals work more efficiently.
You can apply your knowledge to work on the following sample machine learning projects:
(a) Outbreak Prevention – Study past disease outbreak data on a small scale (like a community) level to predict the outbreak of diseases. A simple example is to predict when members of a community are susceptible to the flu based on past data.
(b) Diagnostic Care – Study medical results and automatically classify them based on type.
(c) Insurance Corrections – Study risk factor based on past medical data to accurately adjust insurance premiums per individual.
6. Enron Scandal Analysis
If you have not heard of the Enron Scandal, do give it that article a read! Enron was one of the largest energy companies in America in 2000 when it was exposed to fraud which ultimately led to its bankruptcy and closure.
How the scandal helped ML engineers is that it gave us a rich database of over 500,000 emails between 150 former Enror senior executives.
This data is being used by programmers to build multiple machine learning projects, mainly because the emails are real making projects more challenging and accurate. You can use this data to build the following projects:
(a) Detect Anomalies – Build a program to analyze emails leading up to the scandal to identify anomalies that can be used to predict such instances in the future.
(b) Network Analysis – Build a program to map employe networks to identify the influencers of the scandal.
(c) Natural Learning Processing – Analyze behavior and language to classify emails into different categories, which can be used to predict future scandals.
7. Classification of Iris Flowers
This is a very good project for beginners. The goal is to build a machine learning-enabled program that is able to classify different flowers into 3 categories of species: setosa, virginica, and versicolor. Classification is made based on the width and length of flower petals and sepals.
The available Iris flowers dataset is not massive making it easy for beginners to work with and is one of the best data sets available in classification literature.
This dataset comprises numeric attributes of flowers and programmers need to manage the loading and handling of data.
8. Movie Recommender with Movielens Dataset
This is an extremely interesting project especially since every programmer is aware of movie recommenders. If you have used Netflix or Hulu (you have definitely used YouTube) you know what a video recommender is. It recommends videos you might find interesting based on what you have previously watched.
For this project, you will use the Movielens Dataset which is available online. It consists of around 1,000,209 movie ratings of 3,900 movies created by around 6,040 Movielens users.
9. Program to Predict the Quality of Wine
Wine gets better with age, we all know that. But did you know, along with age, there are many other factors that influence and improve the quality of wine? Physiochemical tests like alcohol quantity, fixed acidity, volatile acidity, determination of density, and pH are used to test and provide wine quality certification.
In this project, you will build a machine learning-enabled program to predict the quality of a wine by analysing its chemical properties. The wine quality dataset we have linked below contains 4898 observations with 11 independent and 1 dependent variable.
10. Machine Learning Program to Predict Boston Housing Price Future
The Boston House Prices Dataset we linked below contains prices of numerous houses located around Boston. It contains information on the age of the owners, area crime rate, non-retail businesses in the area, and other attributes (total of 14) that you can use. The dataset is provided by the UCI Machine Learning Repository.
This project will be a program that analyses past data to predict the future of housing prices in Boston.
11. Human Activity Analysis and Prediction
We have provided a smartphone usage dataset below which contains the fitness activity of 30 different people captured via smartphones with inertial sensors. The project is to build an ML-enabled program to identify human fitness activity, classify it, and predict future activity.
Why We Stress on Learning Via Machine Learning Projects
Taking up Machine Learning as a career can be challenging, and cracking interviews is not as easy as it is with other fields. Companies working on Machine Learning rely on technology for predictive data that enables them to make critical decisions.
This increases the demand for proficient ML engineers and increases the earning potential, but also the difficulty of cracking interviews.
Learning through experience is the best way to solidify knowledge. We have accumulated the above 11 projects along with example projects, learning tutorials, and data sets to make the process of building the project easier for you. As you work on each project, you will learn how to apply theoretical concepts to practical activities and improve.
If you are inspired by the opportunity provided by Machine Learning, enroll in Digital Vidya’s Machine Learning Course today.
