A Quick Guide to Machine Learning for Beginners

Introduction To Machine Learning 

The field of Data Science has grown rapidly over the past decade and has become more vital to society. In today’s world, where huge amounts of data are generated every day, people who are capable of making sense of and interpreting these data are in greater demand. According to the Bureau of Labor Statistics, computer and information research jobs, including Data Scientist positions, are projected to grow 22% between 2020 and 2030. 

Data scientists often use the terms Machine Learning and Artificial Intelligence interchangeably when referring to Data Science, but they are not synonymous, although they do seem to be closely related. As a subset of artificial intelligence, Machine Learning is an approach in which data scientists provide machines with data and then allow them to learn for themselves.  

This article on Machine Learning guide will briefly overview the introduction to Machine Learning with python: a guide for data scientists. 

What Is Machine Learning? 

Now it’s time to discuss the meaning of Machine Learning with respect to Data Science. As part of the field of computer science, Machine Learning is the process by which computers can automatically infer patterns from data without being explicitly asked to do so. There is often a need to make inferences from the statistics of the data using algorithms, which facilitates the automatic analysis of the statistical properties of the data and the creation of mathematical models that represent the relationship between the different quantities in the data. 

Comparatively, this is in contrast to traditional computing, which is based on deterministic systems, where we explicitly tell the computer a set of rules that must be followed for it to carry out a specific task. It is commonly referred to as rule-based programming when it comes to programming computers. A key difference between Machine Learning and rules-based programming is that Machine Learning has the capability to learn and infer rules on its own, whereas rules-based programming does not. 

For example, suppose you are a bank manager, and you wish to figure out whether or not a loan applicant is likely to default on their loan by the end of the year. If the bank manager were to adopt a rules-based approach, the computer would be explicitly told that if the applicant’s credit score is below a specified threshold, then the application should be rejected. 

The historical data on credit scores and the outcomes of loans for figuring out what the appropriate threshold alone would be a Machine Learning for beginners algorithm. By doing so, the machine is able to learn from historical data and create its own rules based on that data. 

Obviously, this is only an introduction to Machine Learning since a real-world Machine Learning model is generally far more complex than a simple threshold applied to a data set. But still, it is an excellent example of how a Machine Learning guide can be incredibly useful in various situations. 

Regardless of your organization’s KPIs, you can optimize them as long as you have the relevant data. It may be possible, for instance, if you had access to a historical dataset of your customers, to predict which of your current customers are at risk of leaving, which will allow you to prevent churn before it occurs. 

A lot of progress has been made in the field of Machine Learning in the last few years, and there is much more that can be done than that. All are based on Machine Learning algorithms, from voice recognition to self-driving cars to automated email filtering systems that flag spam in your email inbox. These algorithms form the basis of many of the technological breakthroughs that we are relying on. 

Types Of Machine Learning 

Let’s explore various types of Machine Learning in detail below: 

Supervised Learning 

Supervisory Machine Learning is an algorithm class that uses data labeled explicitly for the quantity that is of interest (the target or response is sometimes referred to as this quantity). 

Using semi-supervised learning, AI models are trained using both labeled and unlabeled data in conjunction with each other. 

The data labeling process is necessary if you are dealing with unlabeled data. During the training of the Machine Learning model, labeling is used to help annotate examples to assist in the training of the model. Humans are usually responsible for labeling, which can be time-consuming and expensive since it requires a lot of energy. It is, however, possible to automate the process of labeling in a number of ways. 

For example, suppose you are a bank manager, and you wish to figure out whether or not a loan applicant is likely to default on their loan by the end of the year. If the bank manager were to adopt a rules-based approach, the computer would be explicitly told that if the applicant’s credit score is below a specified threshold, then the application should be rejected. 

In this case, we had historical information about the credit scores of past loan applicants (and possibly their income levels, age, and so forth) alongside explicit labels that indicated whether the individual in question had defaulted on their loan or not. 

Unsupervised Learning 

Unlike supervised learning problems, unsupervised learning relies simply on patterns found in the data. Consider Amazon as an example. Are there any clusters (groups of similar customers) that can be identified based on the customers’ purchase history? 

We can make recommendations based on what other people in the cluster also purchased, even though we do not have explicit, definitive information about a person’s interests, just by identifying a particular group of customers who purchase similar items. The “you might also like” carousel on Amazon uses similar systems. 

Based on the similarity in behavior patterns, K-means clustering categorizes different groups of customers into different clusters. In terms of technical detail, each cluster is found by finding its centroid, which is then used as the cluster’s initial mean. New customers are assigned to clusters based on their similarities to other cluster members. 

We could also study the characteristics of the clusters once we’ve identified them. Consider, for example, a particular cluster that is buying a lot of video games. If that is the case, even though no one explicitly told us, we can infer that this group of customers is a gaming audience. 

We might be able to use the labels from unsupervised learning to develop supervised learning models once we have done this sort of analysis. We might, for example, be able to predict, based on the labels from unsupervised learning, how much money a 20-year-old gamer will be willing to spend with us based on their activity level as compared to a 40-year-old fishing enthusiast. 

Reinforcement Learning 

This type of algorithm is part of a class of Machine Learning algorithms where an agent is given a particular task to complete without being given too much guidance about how to accomplish it. 

A rather different approach is to allow the computer to make its own decisions, and then we assign penalties and rewards based on whether or not the choices it makes lead to the result we would like to see. By repeating the process multiple times, the computer can learn the optimal way to do something through trial and error and repeated iterations, allowing it to learn the correct way to do things. In a way, it feels almost as if the computer is playing a video game and making discoveries as it goes along. 

The most astonishing results of reinforcement learning can be observed in the application of playing games, which is precisely the application where the concept has shown the best results. With the help of reinforcement learning, Google built the world’s most infamous Go model, AlphaGo, which, despite its low ranking, was able to beat even the best human players in chess. 

Deep Learning 

An artificial neural network, or an ANN, is used to perform deep learning, specifically a convolutional neural network. Deep neural networks, recurrent neural networks, convolutional neural networks, and deep belief networks are some of the architectures used in deep learning. 

Bioinformatics, medical image analysis, games, and drug design all use these networks to solve natural language processing, speech recognition, computer vision, and other problems. Deep learning is also applied in several other fields. It requires a great deal of processing power and a huge amount of data, which is generally easy to obtain these days. 

Machine Learning Applications 

Now we will discuss the application of Machine Learning broadly under the following points: 

Regression 

Using regression would be helpful if you were trying to predict continuous values, such as the cost of a house or the outside air temperature in degrees, for instance. Since the value can be any number with no restrictions, it is not possible for this type of problem to have a specific value constraint. 

Classification 

You would use classification if you wanted to predict discrete values, such as by categorizing things into different categories. The answer to a problem such as, “Will he make this purchase?” will fall into one of two specific categories: a yes or a no answer. Alternatively, this is known as a binary classification problem. 

Real-world Examples Of Machine Learning 

Listed below are some real-world examples of Machine Learning: 

• Image recognition: Real-world examples of Machine Learning include image recognition, which is well known and widespread. The intensity of the pixels can identify images in black and white and in color. 
• Predictive analytics: By classifying data into groups, Machine Learning can then be used by analysts to define rules to categorize the data. A fault probability can be calculated once the classification is complete. 
• Speech recognition: Speech can be translated into text using Machine Learning. Using certain software applications, you can convert recorded speech and live voice into text files. Also, time-frequency bands can be used to segment speech. 

Conclusion 

With the advancement of technology, Machine Learning scope and definition are constantly evolving. New applications and resources are developed to deploy Machine Learning’s power in a broader population, and its accessibility and utilization are continuously analyzed, assessed, and improved.

In this article, we tried to provide a brief introduction to Machine Learning with python: a guide for data scientists. If you’re willing to learn more about Machine Learning and its real-life significance, then you need to check out the Data Science and Machine Learning course for beginners by UNext.