Machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves the development of algorithms that can analyze and learn from data, making decisions or predictions based on this data.
Common misconceptions about machine learning
ML is the same as AI. In reality, ML is a subset of AI. While AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart,” ML is a specific application of AI where machines can learn from data.
ML can learn and adapt on its own. In reality, ML models do learn from data, but they don't adapt or evolve autonomously. They operate and make predictions within the boundaries of their programming and the data they are trained on. Human intervention is often required to update or tweak models.
ML eliminates the need for human workers. In reality, while ML can automate certain tasks, it works best when complementing human skills and decision-making. It's a tool to enhance productivity and efficiency, not a replacement for the human workforce.
ML is only about building algorithms. In reality, algorithm design is a part of ML, but it also involves data preparation, feature selection, model training and testing, and deployment. It's a multi-faceted process that goes beyond just algorithms.
ML is infallible and unbiased. In reality, ML models can inherit biases present in the training data, leading to biased or flawed outcomes. Ensuring data quality and diversity is critical to minimize bias.
ML works with any kind of data. In reality, ML requires quality data. Garbage in, garbage out – if the input data is poor, the model's predictions will be unreliable. Data preprocessing is a vital step in ML.
ML models are always transparent and explainable. In reality, some complex models, like deep learning networks, can be "black boxes," making it hard to understand exactly how they arrive at a decision.
ML can make its own decisions. In reality, ML models can provide predictions or classifications based on data, but they don't "decide" in the human sense. They follow programmed instructions and cannot exercise judgment or understanding.
ML is only for tech companies. In reality, ML has applications across various industries – healthcare, finance, retail, manufacturing, and more. It's not limited to tech companies.
ML is a recent development. In reality, while ML has gained prominence recently due to technological advancements, its foundations were laid decades ago. The field has been evolving over a significant period.
Building blocks of machine learning
We can state that machine learning consists of certain blocks, like algorithms and data. What is their role exactly?
Algorithms are the rules or instructions followed by ML models to learn from data. They can be as simple as linear regression or as complex as deep learning neural networks. Some of the popular algorithms include:
Linear regression – used for predicting a continuous value.
Logistic regression – used for binary classification tasks (e.g., spam detection).
Decision trees – A model that makes decisions based on branching rules.
Random forest – An ensemble of decision trees typically used for classification problems.
Support vector machines – Effective in high dimensional spaces, used for classification and regression tasks.
Neural networks – A set of algorithms modeled after the human brain, used in deep learning for complex tasks like image and speech recognition.
K-means clustering – An unsupervised algorithm used to group data into clusters.
Gradient boosting machines – Builds models in a stage-wise fashion; it's a powerful technique for building predictive models.
An ML model is what you get when you train an algorithm with data. It's the output that can make predictions or decisions based on new input data. Different types of models include decision trees, support vector machines, and neural networks.
What’s the role of data in machine learning?
Data collection. The process of gathering information relevant to the problem you're trying to solve. This data can come from various sources and needs to be relevant and substantial enough to train models effectively.
Data processing. This involves cleaning and transforming the collected data into a format suitable for training ML models. It includes handling missing values, normalizing or scaling data, and encoding categorical variables.
Data usage. The processed data is then used for training, testing, and validating the ML models. Data is crucial in every step – from understanding the problem to fine-tuning the model for better accuracy.
Tools and technologies commonly used in ML
Python and R are the most popular due to their robust libraries and frameworks specifically designed for ML (like Scikit-learn, TensorFlow, and PyTorch for Python).
Data Analysis Tools: Pandas, NumPy, and Matplotlib in Python are essential for data manipulation and visualization.
Machine Learning Frameworks: TensorFlow, PyTorch, and Keras are widely used for building and training complex models, especially in deep learning.
Cloud Platforms: AWS, Google Cloud, and Azure offer ML services that provide scalable computing power and storage, along with various ML tools and APIs.
Big Data Technologies: Tools like Apache Hadoop and Spark are crucial when dealing with large datasets that are typical in ML applications.
Automated Machine Learning (AutoML): Platforms like Google's AutoML provide tools to automate the process of applying machine learning to real-world problems, making it more accessible.
Three types of ML
Machine Learning (ML) can be broadly categorized into three main types: Supervised learning, Unsupervised learning, and Reinforcement learning. Let's explore them with examples
Supervised learning
In supervised learning, the algorithm learns from labeled training data, helping to predict outcomes or classify data into groups. For example:
Email spam filtering. Classifying emails as “spam” or “not spam” based on distinguishing features in the data.
Credit scoring. Assessing credit worthiness of applicants by training on historical data where the credit score outcomes are known.
Medical diagnosis. Using patient data to predict the presence or absence of a disease.
Unsupervised learning
Unsupervised learning involves training on data without labeled outcomes. The algorithm tries to identify patterns and structures in the data. Real-world examples:
Market basket analysis. Identifying patterns in consumer purchasing by grouping products frequently bought together.
Social network analysis. Detecting communities or groups within a social network based on interactions or connections.
Anomaly detection in network traffic. Identifying unusual patterns that could signify network breaches or cyberattacks.
Reinforcement learning
Reinforcement learning is about taking suitable actions to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path in a specific context. These are some examples:
Autonomous vehicles. Cars learn to drive by themselves through trial and error, with sensors providing feedback.
Robotics in manufacturing. Robots learn to perform tasks like assembling with increasing efficiency and precision.
Game AI. Algorithms that learn to play and improve at games like chess or Go by playing numerous games against themselves or other opponents.
How do we use ML in real life?
Predictive analytics is used in sales forecasting, risk assessment, and customer segmentation.
Customer service. Chatbots and virtual assistants powered by ML can handle customer inquiries efficiently.
Fraud detection. ML algorithms can analyze transaction patterns to identify and prevent fraudulent activities.
Supply chain optimization. Predictive models can forecast inventory needs and optimize supply chains.
Personalization. In marketing, ML can be used for personalized recommendations and targeted advertising.
Human resources. Automating candidate screening and using predictive models to identify potential successful hires.
Predicting patient outcomes in healthcare
Researchers at Beth Israel Deaconess Medical Center used ML to predict the mortality risk of patients in intensive care units. By analyzing medical data like vital signs, lab results, and notes, the ML model could predict patient outcomes with high accuracy.
This application of ML aids doctors in making critical treatment decisions and allocating resources more effectively, potentially saving lives.
Fraud detection in finance and banking
JPMorgan Chase implemented an ML system to detect fraudulent transactions. The system analyzes patterns in large datasets of transactions to identify potentially fraudulent activities.
The ML model helps in reducing financial losses due to fraud and enhances the security of customer transactions.
Personalized shopping experiences in retail
Amazon uses ML algorithms for its recommendation system, which suggests products to customers based on their browsing and purchasing history.
This personalized shopping experience increases customer satisfaction and loyalty, and also boosts sales by suggesting relevant products that customers are more likely to purchase.
Predictive maintenance in manufacturing
Airbus implemented ML algorithms to predict failures in aircraft components. By analyzing data from various sensors on planes, they can predict when parts need maintenance before they fail.
This approach minimizes downtime, reduces maintenance costs, and improves safety.
Precision farming in agriculture
John Deere uses ML to provide farmers with insights about planting, crop care, and harvesting, using data from field sensors and satellite imagery.
This information helps farmers make better decisions, leading to increased crop yields and more efficient farming practices.
Autonomous driving in automotive
Tesla's Autopilot system uses ML to enable semi-autonomous driving. The system processes data from cameras, radar, and sensors to make real-time driving decisions.
While still in development, this technology has the potential to reduce accidents, ease traffic congestion, and revolutionize transportation.
Mobile app development
Mobile app development is the process of creating software applications that run specifically on smartphones and tablets. It is like building a toolbox for your phone. Each app is a specialized tool that helps you do something specific, from entertainment to communication to getting work done.
Key terms of mobile app development
Mobile app. A small software program designed for a mobile device. Imagine it as a mini-tool with a specific function, like a game, a fitness tracker, or a shopping app.
Development. The act of creating something new. In this case, it's the process of writing code, designing the app's interface, and making it work smoothly.
Smartphones and tablets. These are small, portable computing devices with touchscreens. They are more powerful than basic phones and can run various apps.
A bit of history
The first mobile app was created in the 1990s. However, due to differences in the “mobile app” definition, there are two contenders for the first-ever mobile app.
Built-in apps. In 1994, the IBM Simon Personal Communicator was considered the first true smartphone. It came pre-loaded with basic apps like Calendar, Address Book, Email, Calculator, and even a Notes app. These weren't downloaded from a store but rather built into the phone's system.
Downloaded game. Another contender is the game "Snake" pre-installed on the Nokia 6110 phone in 1997. This was a downloadable game in the sense it wasn't part of the phone's core functionality, but it wasn't downloaded from an app store either.
Want to feel old? (Or way too young?) Check out a YouTuber playing Snake on two Nokia 6110 phones:
[embed]https://www.youtube.com/watch?v=j9Rk4JQwtL4&ab_channel=unlokia[/embed]
What’s the difference between the first mobile apps and the mobile apps of the 2020s?
Mobile apps have gone from basic tools to powerful mini-computers in your pocket. They offer a wider range of functionalities, leverage the power of the internet, prioritize user experience, and benefit from advanced development tools. Here's a breakdown of the key differences between the first mobile apps and those of the 2020s:
Functionality
Early apps were focused on basic functionalities like calendars, address books, or simple games. They were limited by the processing power and storage capacity of early phones. 2020s apps offer a vast array of features and functionalities, from complex gaming experiences and advanced productivity tools to social media platforms and AI-powered assistants.
Connectivity
Early apps mostly functioned offline due to limited internet connectivity on early phones. 2020s apps often rely heavily on internet connectivity to deliver their full features. They leverage cloud storage, real-time updates, and integration with other online services.
Accessibility
Early apps were available on a limited number of phone models with varying operating systems. 2020s apps are designed to work across various phone models with standardized operating systems (like Android and iOS). They are readily available through centralized app stores with millions of options.
User Interface
Early apps had basic text-based interfaces or simple menus with limited graphics. 2020s apps prioritize intuitive and user-friendly interfaces with rich visuals, touch-optimized controls, and interactive features.
Development
Early apps were developed with simpler programming languages and limited tools. 2020s apps are developed using advanced programming languages, frameworks, and sophisticated tools that allow for complex functionalities and faster development cycles.
What are the development platforms (iOS and Android)?
Imagine you're building a house. You wouldn't use random bricks and mortar without a plan or the right tools, would you? In mobile app development, development platforms are like the blueprints and toolkits that help you build your app.
Development platforms are software environments that provide the tools and resources needed to create mobile applications. They offer pre-written code libraries, development tools, and frameworks to streamline the process. There are two main categories for mobile app development platforms.
Native platforms
They are designed specifically for a particular operating system (OS), such as iOS or Android. This allows for the best performance and access to all the features of that OS.
iOS development uses Xcode, Apple's IDE, and the Swift programming language to build apps for iPhones and iPads.
Android development uses Android Studio, another IDE, and primarily the Kotlin programming language to build apps for Android smartphones and tablets.
Cross-platform development
These platforms allow you to create apps that run on multiple operating systems with a single codebase. This saves development time and resources but may have limitations in performance or access to certain device features. Among the most popular cross-platform development frameworks are React Native (which uses JavaScript to build apps that look and feel native on iOS and Android) and Flutter (which uses the Dart programming language to create visually appealing apps for various platforms).
Ten 2024 mobile app development trends
Mobile app development is constantly evolving, with new technologies and user expectations shaping how apps are built. Here are some of the hottest trends for 2024:
1. Foldables
Foldable phones are no longer a novelty, and apps are being optimized to take advantage of their unique form factor. Expect to see interfaces that adapt and expand on larger foldable screens, creating immersive user experiences.
2. Wearables
Smartwatches and other wearables are becoming increasingly sophisticated. We'll see a surge in apps designed specifically for these devices, focusing on health and fitness tracking, mobile payments, and voice-activated controls.
3. IoT
Connecting everyday devices to the internet is creating a network of opportunities. Apps will play a central role in controlling smart homes, managing connected appliances, and personalizing user experiences based on real-time data.
4. AR and VR
With advancements in AR and VR technology, expect to see these features integrated into more apps. Imagine furniture shopping with AR to see how a couch fits in your living room or attending virtual concerts and events from anywhere in the world.
5. 5G
The rollout of 5G networks will unlock faster download speeds and lower latency. This paves the way for apps that require high bandwidth, like real-time video streaming, cloud gaming, and AR/VR experiences with minimal lag.
6. AI
Expect to see AI-powered chatbots offering customer service, personalized recommendations within apps, and voice assistants becoming even more intuitive and helpful.
7. Cost-conscious development
With the growing popularity of low-code/no-code development platforms, the barrier to entry for app creation is lowering. These platforms allow even those with limited coding experience to build basic apps, making development more accessible and potentially more affordable.
8. User privacy and security
Expect apps to prioritize robust security features and transparent data collection practices.
9. Super apps
Super apps are all-encompassing platforms offering a range of services, from messaging and payments to local business listings and transportation options. We might see a rise of these in specific regions.
10. Focus on mental wellbeing
Apps promoting mental health and well-being are expected to grow in popularity, offering tools for stress management, meditation, and sleep improvement.
