Basics of Machine learning: Types and Applications

Machine learning is the most significant advancement since the microprocessor. Those who had been wondering what machine learning was have finally learned it isn't science fiction, but rather a reality. Artificial intelligence advancements are being paved by technologies such as neural networks.

 

Machine learning technology has made people more productive, healthier, and happier than ever before. Machine learning, according to industry executives, is ushering in a new phase of the industrial revolution.

 

(Must catch: What is Artificial Intelligence?)

 

So, let's cover the basics of machine learning and its applications.

 

 

Machine learning

 

Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and develop on their own without having to be explicitly programmed. Machine learning is concerned with the creation of computer programmes that can access data and learn on their own. Moreover, 

 

• Machine learning is essential because it allows businesses to see trends in customer behaviour and company operating patterns while also assisting in the creation of new goods. 

• Machine learning is at the heart of many of today's most successful businesses, like Facebook, Google, and Uber. For many businesses, machine learning has become a key competitive difference.

• Machine learning is utilised in many apps on our phones, including search engines, spam filters, websites that provide personalised recommendations, banking software that detects odd transactions, and speech recognition.

 

(Must read: Machine learning tutorial)

 

 

Steps used in Machine Learning

 

There are 5 basic steps used to perform a machine learning task:

 

1. Data collection: Whether it's raw data from Excel, Access, or text files, this phase (collecting historical data) lays the groundwork for future learning. The machine's learning chances improve as the variety, density, and amount of relevant data increases.

2. Data arrangement: The quality of the data utilised in any analytical procedure is critical. It is necessary to devote effort to assessing the data's quality and then take actions to address concerns such as missing data and outlier handling. Exploratory data analysis is one technique of delving into the subtleties of the data in more depth, therefore increasing the nutritional value of the data.

3. Training a model: This is the stage where the machine learning algorithm is trained by feeding datasets. This is the stage where the learning takes place. Consistent training can significantly improve the prediction rate of the ML model. The weights of the model must be initialized randomly. This way the algorithm will learn to adjust the weights accordingly.

4. Evaluating the model: The second portion of the data (holdout /test data) is utilised to check the correctness. This stage influences the accuracy of the algorithm selection based on the result. A better way to verify a model's correctness is to run it on data that was not utilised at all during the model's development.

5. Improving the performance: This phase might entail switching to a new model or adding extra variables to improve efficiency. As a result, data gathering and preparation must take up a substantial amount of time.

 

(Suggested blog: Machine learning models)

 

Key Elements of Machine Learning

 

There are tens of thousands of machine learning algorithms and hundreds of new algorithms are developed every year.

Every machine learning algorithm has three components:

 

• Representation: the main aim is to represent knowledge here. Examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles and others.

• Evaluation: the method for assessing potential programmes (hypotheses). Accuracy, prediction, and recall are examples, as are squared error, likelihood, posterior probability, cost, margin, and entropy k-L divergence.

• Optimization: The search process is the method through which candidate programmes are created. Combinatorial optimization, convex optimization, and restricted optimization are among examples.

 

 

3 Types of Machine Learning

 

Since machine learning is so complicated, it's been separated into three categories: supervised, unsupervised and reinforcement learning. Each one has a certain function and performs a specific activity, producing outcomes and employing various types of data.

 

Supervised learning accounts for around 70% of machine learning, with unsupervised learning accounting for the remaining 10% to 20%. Reinforcement learning takes up the rest of the time. 

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Types of machine learning

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1. Supervised Learning

 

The training type supervised learning refers to when the machine is monitored while it is "learning." But what does it mean to supervise a machine? It means that we provide the computer a lot of information about a case as well as the conclusion of the case. The result is referred to as labelled data, while the other information is referred to as input characteristics.

 

For instance, we show the computer 1000 instances of consumers defaulting on a loan and 1000 instances of customers not defaulting. All other attributes, such as age, salary, loan amount, outstanding amount, other loan history, and so on, are input features, whereas default / not-default is the outcome and therefore the labelled data. 

 

The machine is supervised by the labelled data in this example in order to understand what the connections and dependencies are between the default outcome and the borrower information.

 

 

2. Unsupervised Learning

 

Unsupervised learning, also known as unsupervised machine learning, analyses and clusters unlabeled information using machine learning techniques. Without the need for human interaction, these algorithms uncover hidden patterns or data groupings. It is the best option for exploratory data analysis, cross-selling tactics, consumer segmentation, and picture identification because of its capacity to identify similarities and contrasts in information.

 

Unsupervised learning may uncover hidden and unknown patterns in data, assisting in the discovery of features that are critical for data auto-categorization. Furthermore, unlabeled data is readily available from a variety of sources. 

 

For example, in a corpus of text, an unsupervised learning algorithm may detect similar patterns and categorise the texts into unknown categories, assisting the user in identifying Topics included in the text – such as what a particular product review is discussing, and so on.

 

 

3. Reinforcement learning

 

Reinforcement learning happens when the algorithm is presented with instances that aren't labelled, as opposed to unsupervised learning. However, you may give positive or negative feedback to an example depending on the algorithm's suggested solution. 

 

Reinforcement learning is linked to applications in which the algorithm must make judgments (rather than merely describe, as in unsupervised learning), and those decisions have consequences. It's similar to learning by trial and error in the human world.

 

Errors aid learning since they come with a cost (money, effort, regret, suffering, and so on), teaching you that some actions are less likely to succeed than others. When computers learn to play video games on their own, this is an intriguing example of reinforcement learning. (from)

 

(Related blog: Different types of machine learning)

 

 

5 Application of Machine Learning

 

Now in this Machine learning tutorial, let's learn the applications of Machine Learning:

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Applications of machine learning

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1. Augmentation

 

Machine learning is a type of artificial intelligence that helps humans with their day-to-day work, whether individually or economically, without having total control over the outcome. Machine learning is utilised in a variety of applications, including virtual assistants, data analysis, and software solutions. The primary goal is to decrease human bias-related mistakes.

 

2. Automation

 

Machine learning is a type of artificial intelligence that can function independently in any sector without the need for human interaction. In manufacturing factories, for example, robots execute critical processes.

 

3. Speech Recognition:

 

Speech recognition, often known as "Speech to text" or "Computer speech recognition," is the process of translating voice commands into text. Machine learning methods are now widely employed in a variety of voice recognition applications. Speech recognition technology is used by Google Assistant, Siri, Cortana, and Alexa to obey voice commands.

 

4. Traffic prediction:

 

Google Maps, for example, can forecast traffic conditions such as whether it is clear, sluggish moving, or highly congested in two ways:

 

• Google Maps and sensors provide real-time car position.
• At the same time, average time has been taken on previous days.

 

Everyone who uses Google Map contributes to the app's improvement. To enhance speed, it collects data from the user and transmits it back to its database.

 

(Also read: How do Google maps work?)

 

5. Online Customer Support

 

A growing number of websites now allow users to speak with customer service professionals while exploring the site. However, not every website has a live representative to respond to your questions. 

 

In most situations, you'll be conversing with a chatbot. These bots are designed to extract data from websites and deliver it to customers. In the meanwhile, the ML chatbots improve with time. They have a better understanding of user queries and provide better responses as a result of its machine learning algorithms. (Source)

 

 

Conclusion

 

These are the fundamental ideas addressed in most machine learning classes and the first few chapters of any excellent textbook on the subject. It is helpful to have a strong understanding of these ideas as a practitioner in order to better grasp how machine learning algorithms operate in general.