Have you ever wondered how satisfied consumers would be if all companies make data-driven decisions to improve and personalize the customer experience? Machine learning (ML) has the power to transform the customer experience by keeping a history of their preferences and making recommendations, understanding speech and text to ease customer navigation experience with the company site, and much more. The age has come when more and more companies realize the priceless value of incorporating machine learning and AI to streamline their operations, ultimately improving productivity and customer experience. Thus, it should not come as a surprise that most companies have allocated substantial resources towards AI and ML initiatives. This development has made machine learning one of the most sought-after skills and a serious consideration for professionals seeking to scale their careers in any field owing to the multidisciplinary benefits of AI.
Past undertaking AI and ML courses, a rich portfolio of machine learning projects demonstrate a professional’s practical knowledge and ability to apply ML concepts in real-world situations, which essentially is what recruiters are keen on. Are you wondering which projects you can undertake as a beginner to hone your ML skills? Consider working on the projects below.
- Market basket analysis with python
Dataset: Groceries Market Basket Dataset
The Market Basket Analysis is a retail sales and marketing strategy that links products that are in most cases usually bought together. This is done through analysis of datasets using the Association Learning Rule, which is a rule-based Machine Learning approach that employs data mining techniques. Its benefits in real-life situations are that retailers can offer enhanced customer experience by packaging correlated products together or even offering strategic discounts to boost the sale of either of the associated products.
This simple but valuable project enables the beginner especially to have a stronger grasp of the algorithms Apriori, Eclat, and FP-Growth, which are the three most used in Market Basket Analysis.
- Fake news classification model
Dataset: Fake and Real News Dataset
With a remarkable increase in the number of people accessing the internet through various devices, a staggering 2.5 quintillion bytes of data is generated every passing day. Also on the rise is the circulation of untrue or inaccurate news. As if this is not enough, the spread of false or inaccurate information can be catastrophic to society since most major decisions by organizations, governments, and individuals are data-based. For instance, inaccurate data may lead to misdiagnosis of patients; fake news may lead to uncalled-for wars, and in both cases, innocent deaths.
Being a classification problem, practice building a model using python language that can accurately classify the news or information into real or fake. To a beginner, this basic project will help apply and sharpen their understanding of concepts like vectorization, as well as the algorithm Passive Aggressive Classifier, which is often used in fake news detection.
- Writing an algorithm from scratch
This is the most holistic approach to sharpening fundamental machine learning skills and evaluating your mastery in generating algorithms. It is helpful especially to a beginner because it reinforces understanding of the fundamental stages of algorithm writing which are:
- Identifying and describing the problem
- Analyzing the problem
- Developing a basic but functional algorithm
- Refining it with more details
- Reviewing it for functionality
- Disease prediction
The health sector is a high-stakes field with long hours of training and many years of education hence one of the greatest beneficiaries of machine learning. ML can be applied to significantly enhance competence and reduce workload challenges for doctors to allow them to concentrate on more complex human-oriented tasks.
This disease predictor is another python-based project that uses algorithms to predict diseases and enhance efficiency in the sector, more so in the areas of preventive care, diagnostic care, and insurance. The dataset majorly comprises the patient’s health data, treatment history, and genetic records.
It helps the user to have a firm grasp of algorithms like the decision tree which is mostly used in this case.
- Uber data analysis using R
Uber being in the service industry with a sensitive operational model, is heavily dependent on big data to make sound decisions. Datasets comprise trips per hour of the day, trips per day of the week, the starting points and destinations of the trips, etc. This data analytics project, therefore, gives the beginner an opportunity to handle big data, which the sustainability of uber depends on. Undertaking this project using R and its numerous libraries enables the learner to:
- Grasp the concepts of R language
- Comprehend general data analytics
- Know how to apply the ggplot2 on uber pickup datasets
- Understand the application of data visualization and thereby the core values of the datasets
- Colour detection with python
Dataset: Eurobot 2018 – Color Order dataset
Colour detection is an easier task for humans than it is for machines. This python project uses the Naive Bayes algorithm with OpenCV and Pandas to construct classifiers through which the various colors are categorized. The dataset consists of the three primary colors i.e., red, blue, and yellow, defined by their values in a data/color matrix. The distance between the colors among themselves within the matrix is then calculated to find the shortest one.
This is an excellent project to get started with computer vision. Color detection models detect colors in an object through that analysis of the light reflected off the surface of the object. Color recognition is important in video production, food and beverage, automotive, and manufacturing industries.
These simple projects aptly prepare the beginner for the practical world. Aspiring ML professionals with such a diverse portfolio as above can boast skills in the following areas:
- Data modeling
- Data evaluation
- Data mining
- ML programming languages
- Hyperparameter tuning
- Predictive analysis
- Regression analysis
- Multivariate calculus
- Linear algebra
As one distinguished professor of the University of Washington, Pedro Domingos rightly said, “The greatest benefit of machine learning may ultimately be not what the machines learn, but what we learn by teaching them”. As a beginner in problem-solving using machine learning, exposure to projects that represent real-life challenges is the initial step that oversees the paradigm shift from the theoretical to the applied knowledge that the world so much needs.
With the above-listed projects, ML professionals should be able to offer their employers real value while also enjoying handsome paychecks and successful careers. As they also solve major world problems, the global economy can thus operate as one balanced, sustainable ecosystem.