In our previous blog post, we explored the evolution and different types of Machine Learning technology. Undoubtedly, Machine Learning has become ubiquitous in our daily lives. In this blog, we aim to delve deeper into Machine Learning algorithms and their current applications in the world. These algorithms are essentially computer programs that examine data and employ patterns to make predictions or classifications. For example, a decision tree algorithm divides data into smaller subsets based on attributes and creates a model that predicts possible outcomes and decisions. As an illustration, a decision tree may forecast the likelihood of a person buying a particular product based on their age, gender, income, and purchase history.
Overall, Machine Learning algorithms provide a powerful tool for analyzing large datasets and making accurate predictions. With the help of these algorithms, businesses can better understand their customers and make more informed decisions. So, if you have ever wondered how companies can predict what you might like to buy or what you might be interested in, machine learning algorithms like decision trees are often the answer.
Machine Learning is All about Which Algorithm to Use With 90% of the world's data being created in the last two years alone (IDC, 2021), the value of discerning patterns has never been more excellent. This makes Machine Learning algorithms the catalyst of informed decision-making, with 80% of businessmen reporting improved operational efficiency by leveraging these algorithms (Deloitte, 2020):
Linear Regression: The fundamental algorithm in a Machine Learning practitioner's toolkit, linear regression, forecasts a numerical value by examining the correlation between dependent and independent variables. This method is the cornerstone of predicting financial market trends, albeit with little excitement.
Logistic Regression: Complementing its linear counterpart, logistic regression is a crucial method for binary classification tasks, such as discerning whether an email is spam or legitimate. It estimates the likelihood of an event by fitting data to a logistic function, which is as captivating as its mathematical foundation implies.
Decision Trees: Imagine a 20-question game by a computerized interrogator, where decision trees partition data into subsets based on feature values, expanding branches and leaves until a prediction is reached. These algorithms apply to various tasks, from diagnosing illnesses to detecting fraudulent transactions.
Support Vector Machines: One of the most robust methods for regression, support vector machines operate by identifying the hyperplane that distinctly separates data points into classes. They are the vanguards of Machine Learning, mitigating overfitting and ensuring that no extraneous data point infiltrates the model.
Neural Networks: The epitome of Machine Learning methodologies, neural networks draw inspiration from human brain functionality and comprise interconnected neurons or “nodes.” Their ability to learn and discern intricate patterns renders them ideal for image/speech recognition and natural language processing.
Machine Learning is More Than a Hypothetic Buzzword Which companies do you envision when asked about Machine Learning? Google, Meta? Microsoft? Of course, these conglomerates are the “Jedi” when it comes to actioning emerging technology, but let’s look at some examples where brands are leveraging Machine Learning:
General Electric is at the forefront of harnessing Machine Learning in the manufacturing industry. Using predictive maintenance algorithms, GE can analyze sensor data from over 50,000 machines and identify potential failures, resulting in a 10-20% reduction in costs and a 25% increase in machine uptime.
In aviation, Rolls-Royce is setting the bar for optimizing operations. The company uses algorithms to analyze over 70 trillion data points collected from 13,000 engines, leading to improved engine maintenance and reduced downtime. Deloitte reveals that this data-driven approach has increased aircraft availability by 20%.
Regarding finance, JPMorgan Chase & Co is using Machine Learning to tackle the ever-present fraud detection and mitigation issue. The global financial institution analyses over 40 petabytes of data daily to identify fraudulent transactions, resulting in a 50% reduction in false positives and a 20% lift in detection accuracy.
The healthcare industry is not immune to Machine Learning either. AstraZeneca, a pharmaceutical company, has adopted Machine Learning algorithms to facilitate drug discovery and development. Take Nature’s word for it, as the Biotechnology report states that it has led to a 50% reduction in identifying new drug candidates.
Finally, we cannot overlook the impact on protein folding prediction as an Alphabet research lab – DeepMind – has made ground-breaking advancements with its AlphaFold system, outperforming competitors in the Critical Assessment of Protein Structure Prediction competition, achieving a GDT score of 92.4 out of 100.
Are There Any Challenges?
And the answer is, “There are, indeed!” As is true of any disruption, businesses must put in place ethical frameworks and guidelines. However, Machine Learning isn’t a silver bullet that needs to be shot at every enterprise obstacle. As we continue to explore the applications of Machine Learning, we will also be challenged with ensuring transparency and accountability in how data is collected and used. From algorithmic data bias to lack of diversity in datasets, our upcoming blog unravels how businesses can overcome the challenges with Machine Learning.
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