What is Machine Learning and How to Use it in Your Mobile App? (UPDATED 2023)

A quintessential task of Machine Learning is delivering personalized recommendations. Recommender systems, a prevalent domain of machine learning, employ historical data to offer tailored suggestions to users. In the realm of Netflix, this system employs collaborative and content-based filtering techniques to propose movies and TV shows based on the viewer's past choices, ratings, and genre preferences.

Evolution of Machine Learning

Machine learning today differs significantly from its earlier iterations due to advancements in computing technology. It originated from the notion that computers can learn autonomously from data, without explicit programming. Researchers exploring artificial intelligence sought to ascertain whether computers could derive knowledge from information.

Machine learning technology has been present since 1952. In the past decade, it has progressed significantly, undergoing pivotal transitions.

A crucial development during the 1990s was the rise of data-driven machine learning. This approach centered on employing sizable datasets to train models and unveil patterns. It represented a shift towards empirical and statistical methods.

Between 1995 and 2005, the focus centered on natural language processing, search algorithms, and information retrieval. Applications in these domains enhanced language comprehension, search engine efficiency, and information organization.

Neural networks, popular in the 1980s, mimic the human brain's structure and function. Their resurgence around 2005, known as deep learning, revolutionized domains like image recognition.

While several machine learning algorithms have existed for years, the recent ability to automate intricate mathematical calculations on extensive datasets in a swift, repetitive manner is a recent innovation.

Types of Machine Learning

Machine learning comes in various flavors, each tailored to specific goals:

Supervised Machine Learning: In this approach, algorithms are fed labeled datasets, learning to predict outcomes based on input features. It's like teaching a computer to recognize patterns by showing it examples of those patterns.

Unsupervised Machine Learning: Here, algorithms delve into unlabeled data, seeking hidden structures and relationships. Think of it as allowing a computer to explore and categorize data on its own.

Reinforcement Learning: Similar to training a pet, this approach involves rewarding machines for correct actions and adjusting their behaviors through trial and error. It's how systems learn to navigate complex environments.

Who's using it?

By extracting insights from big data, often in real-time, various industries enhance efficiency and gain competitive edges. Here are some examples:

1. Financial Services: Machine learning serves dual purposes: uncovering crucial data insights and preventing fraud. This technology identifies investment prospects and aids investors in making strategic trading decisions. Furthermore, machine learning identifies high-risk client profiles and utilizes cyber surveillance to spot fraud indicators.

2. Government: Machine learning detects fraud and curtails the risks of identity theft.

3. Healthcare: Wearable devices with machine learning and sensors provide real-time patient health assessments. This technology assists medical experts in scrutinizing data for patterns and signals that could lead to enhanced diagnoses and treatment plans.

4. Retail: Utilizing machine learning to collect, scrutinize, and personalize shopping experiences, marketing endeavors, pricing strategies, merchandise planning, and customer insights.

5. Oil and Gas: From discovering new energy sources to refining refinery sensor predictions, machine learning optimizes oil distribution for efficiency and cost-effectiveness.

6. Transportation: Efficiency enhancement in transportation relies heavily on data analysis to discern patterns and anticipate issues. Machine learning helps to improve routes, predict challenges, and mitigate traffic congestion.

In summary, machine learning's impact spans diverse sectors, enabling data-driven insights that revolutionize decision-making, streamline processes, and foster innovation.

Difference Between ML and AI

Machine Learning (ML) and Artificial Intelligence (AI) are closely related concepts, but they refer to different aspects of technology and problem-solving. Let's figure it out:

Machine Learning is a subset of AI that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data.

Artificial Intelligence is a broader concept that refers to the development of machines or systems that can perform tasks that typically require human intelligence.

In essence, machine learning is a specific strategy within the field of AI that entails training models on data to make predictions or judgments, whereas AI comprises the larger objective of building robots that may demonstrate human-like intelligence. The majority of the most recent developments in AI have been made possible by machine learning, a foundational technique, although it only makes up a small part of the overall AI picture.

For more information about this, you can learn from IBM Technology video👇

🔍 Make searching faster and easier

Machine learning can significantly improve the search and discovery experience in your mobile app. By analyzing user behavior, search history, and preferences, you can provide more accurate and relevant search results.

You can also incorporate features like voice search, spelling correction, and related suggestions to make the search process faster and more intuitive for users.

💸 Rising sales with machine learning in e-commerce apps

E-commerce apps can greatly benefit from machine learning technologies. Machine learning brings more customers to your app by these means:

• recommendations, selected according to your users’ purchase patterns, searching requests and the site content.
• predictions about future trends, sales, and prices, based on information from open sources (like blogs, social media, news articles etc.).
• optimization of the searching process, which we have previously mentioned.

🕵️‍♀️ Mining big data – find a needle in a haystack

Machine learning can help you make sense of large amounts of data collected from your mobile app. By analyzing user demographics, preferences, and behavior patterns, you can gain valuable insights into user preferences, improve user segmentation, and make data-driven decisions to enhance your app's features and functionality.

🎙️ Benefit from a visual and audio recognition

Machine learning algorithms are capable of recognizing and understanding images, videos, and audio. This opens up opportunities to create mobile apps with features like image recognition, augmented reality, and voice-controlled interfaces.

These technologies can enhance user engagement, provide interactive experiences, and enable new forms of interaction within the app.

One way to use text or audio recognition along with machine learning is to create conversational UX or chatbot. Remember Apple Siri and imagine how excited your users will become if you add something similar to your app.

👁️ Fast and protected authentication process

Machine learning can be used to improve the authentication and security processes in your mobile app. By leveraging technologies like biometric authentication, facial recognition, and behavioral analysis, you can provide a faster and more secure authentication experience for your users. This not only improves user satisfaction but also helps prevent fraud and unauthorized access.

Remember, the easier the authentication process you will make, the more thankful your users will be.

🛡️ Fraud prevention: don’t let your money flow away

Fraud prevention is a growing concern in the mobile app industry. Machine learning algorithms can analyze user behavior, transaction patterns, and other data points to detect and prevent fraud in real-time.

By constantly monitoring and analyzing app activities, you can identify and take action against suspicious activities, protecting your users and your app from potential threats.

One more advantage: traditional apps can resist only already known threats, while machine learning systems allow protecting users from previously unidentified malware attacks in real-time mode. Snapdragon Smart Protect is a good example of such a progressive technology.

🏋️ Turn your fitness app into a personal coach

Machine learning can transform fitness and health apps into personalized virtual coaches. By analyzing user data, such as physical attributes, fitness goals, and workout history, machine learning algorithms can generate customized workout plans, provide real-time feedback, and track progress.

So combining fitness app development services with machine learning is a win-win situation.

👂 Takeaways

In conclusion, we hope this article has provided you with a clearer understanding of machine learning and its transformative potential for your mobile app. As technology continues to advance, integrating machine learning into your app can significantly enhance its capabilities and user experience, ushering in a new era of innovation.

To summarize:

• Machine learning's popularity is on the rise, with a plethora of applications already leveraging it across various industries, from streaming services to government establishments.
• Carefully selecting the right technological stack for your app is crucial, especially in the context of machine learning app development. Approach this decision meticulously to ensure a seamless integration of machine learning capabilities.
• Remember that machine learning presents an opportunity to elevate your app, allowing you to establish a foothold in competitive markets. However, while machine learning is a powerful tool, a compelling idea and adept execution remain equally crucial components.

Feel free to reach out to us if you have any lingering questions on this topic or if you need support with leveraging machine learning in your app.