True comprehension comes not only through the accumulation of knowledge but also from the application of it. The secret to knowing whether you’ve understood any applied aspect of Machine Learning is to implement the knowledge you have earned by working on projects that are as close as possible to the problems being solved in the real world. This will give you a good understanding of what it is like to work in a real-world environment. Australia, the world’s second-largest investor in AI with its major businesses investing an average of $7.9 million in 2016 alone, has created the demand for skills required to implement AI-related solutions to meet business and innovation objectives.
As a result, aspiring professionals wishing to make a career entry or career change into the field of Artificial Intelligence and allied services need to consider undertaking a machine learning course in Sydney, thanks to its geopolitical and economic advantage over the other IT hubs. Now is the opportune time for ML professionals as the industry’s current major challenge is to explore relevant and viable ML-powered ideas and solutions that will keep them a step ahead of the competition.
8 Basic machine learning projects
Our article compiles eight innovative ML applications that have now become a part of us. They make life easy yet businesses rely on them to bring their customers closer to comfort, convenience, and value.
- Wine quality testing
Project dataset: Wine quality dataset
Skills: Data visualization, data exploration, regression models, and R programming
Despite knowing its age, which is the most common parameter used, ascertaining a wine’s quality can be a long shot especially for the layman. Factoring in the numerous physicochemical factors that determine wine quality such as alcohol content, fixed and volatile acidity, density, pH, and others only complicates a task that is already too complex.
This physicochemical information is utilized to develop an ML model that is applied to predict wine quality.
- Predicting stock price
Project dataset: Istanbul Stock Exchange dataset
Skills: Predictive modeling
Considering the volatility and dynamism of data in the stock market, predicting future stock prices can be a daunting task. Too many factors including investor sentiments, demand and supply, geopolitics, and natural calamities influence stock prices. Yet these very factors, thanks to the fact that they are highly unpredictable and uncontrollable, cannot be relied upon to make accurate predictions for stock prices.
Machine learning models that learn as many factors about a company as are fed into the database are your best bet when it comes to making predictions of future stock prices.
- Iris flowers classification
Project dataset: Iris Flowers Classification
The goal of this project is to classify iris flowers into either of the three species Virginica, Setosa, or Versicolor using a machine learning model that learns from the measurements of the already known irises and predicting the species of a new iris flower. Here, datasets mainly contain the flowers’ dimensions indicated in numeric values i.e. length and width of petals and sepals, all given in centimeters. In addition to classification, beginners will learn the basics of loading and handling numerical values and data.
- Movie recommender
Project dataset: https://grouplens.org/datasets/movielens/100k/
Skills: Collaborative filtering and prediction
Modern consumers are increasingly demanding personalized content that suits their tastes and preferences and entertainment movies are no exception. Recommendation systems are developed on the principles of filtration and prediction that typically align to a user’s preferences after using a search system for some time.
These recommendation systems are machine learning applications that mainly rely on user information, comments, and search history. Thus datasets will typically include the history and preferences of individuals as well as all the users collectively, as well as common movies they’ve liked in the past.
- Human activity recognition
Project dataset: Human Activity Recognition using Smartphones dataset
Skills: human activity prediction, multi-classification using ML algorithms
In an era of the internet of things (IoT), numerous wearable devices fitted with sensors have emerged. These devices record data from the environment as well as the user and machine learning systems rely on them to be able to recognize activities that a user is involved in, and in a real-time fashion.
Human activity recognition is useful in the fields of machine learning, computer vision, robotics, and others as it provides crucial information to enable the characterization and categorization of a person’s character, psychological state, and personality based on their activities captured by the human activity recognition system.
The system is basically designed with the ability to study, analyze and classify data, and identify an activity based on the same. Datasets contain data such as body movement, angular velocity, and body inclination.
- Sales forecasting
Project dataset: Walmart Stores Sales Forecasting dataset
Skills: Exploration, data analysis, data visualization
This project’s objective is to create a model that has the predictive capability to estimate future sales volumes. Sales forecast is critical to any business as this information will be useful for customer relationship management, marketing campaigns, and supplier relationship management among others.
Datasets contain past sales, website visits, economic trends, and such like information which with the help of machine learning can be analyzed to make predictions and ultimately informed decisions on effective inventory, workforce, and cash flow management.
- Sentiment analysis
Project dataset: Large movie review dataset
Skills: Data scraping, data mining, and various machine learning models like linear regression, support vector machines, and deep learning
Sentiment analysis is an upcoming field that is proving invaluable to businesses, organizations, and lately, politicians. An in-depth understanding of public sentiments, trends, and opinions paves way for strategic branding, marketing, sales, and even political campaigns with worthy results.
This project uses datasets created using data mined from content pieces like emails, tweets, or any other social media posts. From these datasets, machine learning models are able to make informed analyses and predictions on how the public is bound to react to certain moves based on patterns and history.
- Music recommendation
Project dataset: KKBox’s Music Recommendation Challenge
Skills: deep learning and neural networks
Sites like Airbnb, Amazon, Netflix, Linkedin, and others have found recommender systems to be invaluable for personalized customer relationships and marketing campaigns. The principle behind this specific project is deep learning.
Streaming sites like Spotify, Pandora, and Apple music use music recommendation systems to automatically classify audio songs based on factors like genre, time domain, and content, while also predicting a user’s song preferences based on their listening patterns and habits.
The dataset contains a library of audio songs that have already been frequently listened to by the user, alongside other crucial information e.g. age, race, religion, etc. which can be quite useful too in making predictions and recommendations.
While we have only provided eight machine learning application examples, machine learning uses have penetrated almost all industries and are the power behind the latest trends we see in these industries today. Machine learning is here with us and is part of us yet we have not seen the best of it yet. For professionals in the field thinking about acquiring futuristic skills for their career growth, there is no thinking twice about possessing ML knowledge and skills.