Fundamentals of Machine Learning: Diving into the Basics

Machine learning is a branch of artificial intelligence that has the potential to train a device to make choices based on its analysis of data. Over time, the field has expanded, and machine learning is now becoming a part of decision-making. It is also used in healthcare, finance, marketing, and robotic systems.

What is Machine Learning?

Machine Learning is a part of artificial intelligence, the science of building algorithms that can learn from observing data and make predictions or decisions. While in traditional programming, explicit rules determine how something must behave, in ML models, we train them on data so it’s possible to generalize and adjust.

• Data-Driven: Data is the only winner when using machine learning to predict or make a decision. The difference between traditional programming (where hard-coded rules are used) and ML is that it uses the data to infer insights and relationships where there isn’t any direct correlation to start with. Let’s say a recommendation system runs without predefined logic on the user behaviour data and suggests products to the users.
• Automated Learning: ML models can take in new data without assistance after training. That eliminates the need to update manually all the time. For instance, today’s email spam filters are case insensitive and known to filter through new types of spam emails since they learn from examples that have been labelled.
• Adaptability: Machine learning models can learn their environment and data. For this reason, machine learning is well suited for frequently changing conditions, such as weather forecasting or stock market analysis. The model recalibrates itself as new data comes in and remains accurate.
• Scalability: A vast amount of data must be processed efficiently; therefore, ML algorithms are designed for that. Take a social media platform, for example; it requires millions of posts, images, and interactions daily to deliver a personalized experience using ML models. One of its most valuable features is the ability to handle large-scale data.

Types of Machine Learning

• Supervised Learning: Supervised learning is learning how to turn the model to train it with labelled data. Given a dataset where we know the input/output relationship, the algorithm trains from there and predicts the output for unknown inputs.

• Unsupervised Learning: Labeled data is the topic of supervised learning, while unsupervised learning is with unlabeled data. The algorithm is about searching patterns, structures or relationships in the data with no prior identification of the labels. This type is often used as a cluster or dimensionality reduction.

• Reinforcement Learning: In reinforcement learning, an agent learns by interacting with an environment and receiving feedback through rewards or penalties. The agent aims to maximize the cumulative reward over time by learning an optimal strategy.

Types of Machine Learning Algorithms

Machine Learning (ML) can be categorized into three primary types based on the nature of the data and how algorithms learn from it: Representative Supervised Learning, Unsupervised Learning and Reinforcement Learning. Semi-supervised learning is also considered a hybrid technique. Here’s an in-depth explanation of each type:

• Supervised Learning: The supervised learning algorithms learn by example. That is, for every input, there is a label. We aim to learn a mapping function that will enable the system to accurately predict future unknown data.

Examples of Algorithms

• Linear Regression
• Decision Trees
• Random Forest
• Support Vector Machines(SVMs)

• Unsupervised Learning: Unsupervised learning algorithms take inputs that are not predefined outputs. The main focus of the work is to find patterns, groupings or structures inside of the data.

   Examples of Algorithms

• K-Means Clustering
• Hierarchical Clustering
• Gaussian Mixture Models (GMMs)

• Reinforcement Learning: Unsupervised learning algorithms take inputs that are not predefined outputs. The main focus of the work is to find patterns, groupings or structures inside of the data.

Example of Algorithms

• Q-Learning
• Deep Q-Networks(DQN)
• Policy Gradient Methods

• Semi-supervised Learning: Supervised and unsupervised learning is between supervised and unsupervised. The procedure uses a small amount of labelled data and a substantially larger amount of unlabeled data. When label data is expensive or time-consuming, this method uses the labelled data to guide the learning process.

           Examples of Algorithms

• Self Training
• Co-Training
• Graph-Based Methods

Also Read: Difference Between Artificial Intelligence and Machine Learning

Application of Machine Learning

Machine Learning is being used by almost every sector, ranging from healthcare, marketing, finance, infrastructure, automation, etc. There are some important real-world examples of machine learning, which are as follows:

• Healthcare and Medical Diagnosis: It was pointed out that healthcare industries have adopted machine learning, which assists in creating neural networks. These auto-learning neural nets assist specialists in delivering an effective treatment plan by processing outside information on a patient’s state, radiography, computed tomography, tests, and screenings. Aside from treatment, machine learning also proves beneficial for issues such as automatic billing, clinical decision support, and formulation of clinical clinical care standards, among others.

• Marketing: Machine learning assists marketers in developing hypotheses across various inputs, testing, assessment and analysis datasets. It wishes us blessed to enable swift decision-making based on the concept known as big data. Indeed, even in stock marketing, most transactions are performed through trading robots and are calculated as far as the machine algorithms. Several deep-learning neural networks are useful in constructing trading models, including convolutional neural networks, recurrent neural networks, long-term memory, etc.

• Self-driving cars: This is one of today’s most useful machine-learning trends. It has great significance when it comes to developing self-driving cars. Many automobile industries worldwide, including Tesla, Tata, etc., strive ceaselessly to build self-driving vehicles. It also becomes possible by applying the supervised learning approach, as a machine can learn to identify people and objects while driving.

• Speech Recognition: Speech Recognition is considered one of the most famous uses of machine learning. Currently, virtually all applications for portable devices contain a voice search option. Speech recognition also has this ’’Search By Voice’’ facility embedded in many places. In this method, the voice instructions are transcribed into the text, and this method is called Speech text or ‘Computer speech recognition.

• Traffic Prediction: Another application of machine learning is identifying possible ways to reach the intended destination through Google Maps. It also assists us in calculating traffic conditions, whether clear or jammed, via the current location, as shown by the Google Maps app and sensor.

• Image Recognition: Image recognition is another critical application of machine learning for identifying an object, person, place, etc. Image recognition is most commonly used to auto-friend tagging suggestions on social sites such as Facebook and Instagram. Even when we tag along the photo-sharing website with our Facebook friends, it provides their names based on facial recognition software.

• Product Recommendation: Marketing services in business industries incorporate Machine Learning to promote different products. Every giant and low-ranked organization, including Amazon, Alibaba, Walmart, Netflix, etc., is adopting a machine learning approach for products that recommend their users. Whenever we type any products on their websites, we need to be ready to have many options for similar advertisements. This is also possible, particularly by using Machine Learning algorithms that identify users’ preferences and recommend products to the target user, given past data.

• Automatic Translation: Another major application of machine learning is automatic language translation, which is developed from sequence algorithms by translating text from one language to another preferred language. Google GNMT, which is Google Neural Machine Translation, has this and is named Neural Machine Learning. Besides that, you can also perform language translation on both text on images and complete documents through Google Lens.

• Virtual Assistant: Virtual personal assistant is also a common machine learning use case. First, it enregisters our voice, uploads to the cloud-based server, and decodes that voice using machine learning algorithms. These features have become prominent in every large organisation, such as Amazon, Google, etc., as they are used to play music, call someone, open an app and search data over the internet, etc.

• Email Spam and Malware Filtering: Machine Learning is also useful for sorting numerous e-mails in the receiver’s mailbox. Generally, there are three groups of e-mails: important, normal, and spam. ML algorithms can achieve this, including Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier.

Difference between Machine Learning and Artificial Intelligence.

Conclusion

The principles of machine learning make up the ground on which the contemporary paradigm of artificial intelligence is based, powering the ability to learn, process, and decide. Machine learning can be best defined as a branch of Artificial Intelligence that uses algorithms based on pattern recognition to learn and improve from experience, and its applications range from medicine to the finance and technological sectors.

Knowing concepts such as supervised and unsupervised, as well as reinforcement learning, forms a base for applying machine learning. Meanwhile, as data increases in size and sophistication, machine learning will remain an essential means and a driving force behind new developments for the age of the machine or the digital world. Want to learn more about machine learning and artificial intelligence? You can consider the Integrated Program in Data Science, Machine Learning & Artificial Intelligence Program offered by Hero Vired in collaboration with MIT with Open Learning.