What is Machine Learning?

Machine learning is prevalent in most of the mainstream industries of today. Businesses around the world are scrambling to integrate machine learning into their functions, and new opportunities for aspiring data scientists are growing multifold.

However, there’s a significant gap between what the industry needs and what is currently available. A large number of people are not clear about what machine learning is and how it works. But the idea of teaching machines has been around for a while. Remember Asimov’s Three Laws of robotics? Machine Learning ideas and research have been around for decades. However, there has been a lot of action, developments, and buzz as of recent. By the end of this article, you will understand not only machine learning but also its different types, its ever-growing list of applications, and the latest developments in the domain.

What is Machine Learning?

Machine Learning is the science of teaching machines how to learn by themselves. Now, you might be thinking: Why would we want that? Well, it has a lot of benefits when it comes to analytics and automation applications. The most important of which is:

Machines can do high-frequency repetitive tasks with high accuracy without getting tired or bored.

To understand how machine learning works, let’s take an example of the task of mopping and cleaning the floor. When a human does the task, the quality of the outcome varies. We get exhausted/bored after a few hours of work, and the chances of getting sick also impact the outcome. Depending on the place, it could also be hazardous for a human. On the other hand, if we can teach machines to detect whether the floor needs cleaning and mopping, and how much cleaning is required based on the condition of the floor and the type of floor, machines would perform the same job far better. They can go on to do that job without getting tired or sick!

This is what Machine Learning aims to do! Enabling machines to learn on their own. To answer questions like:

• Whether the floor need cleaning and mopping?
• How long does the floor need to be cleaned?

Machines need a way to think, and this is precisely where machine learning models help. The machines capture data from the environment and feed it to the model. The model then uses this data to predict things like whether the floor needs cleaning or not, or for how long it needs to be cleaned, and so on.

Types of Machine Learning

Machine Learning is of three types:

• Supervised Machine Learning: When you have past data with outcomes (labels in machine learning terminology) and you want to predict the outcomes for the future, you would use Supervised Machine Learning. Supervised Machine Learning problems can again be divided into 2 kinds of problems:
  • Classification Problems: When you want to classify outcomes into different classes. For example, whether the floor needs cleaning or mopping is a classification problem. The outcome can fall into one of the classes – Yes or No. Similarly, whether a customer would default on their loan or not is a classification problem that is of high interest to any Bank
  • Regression Problem: When you want to predict a continuous numerical value. For example, how much cleaning needs to be done? Or what is the expected amount of default from a customer is a Regression problem.
• Unsupervised Machine Learning: Sometimes the goal isn’t prediction! It’s discovering patterns, segments, or hidden structures in the data. For example, a bank would want to segment its customers to understand their behavior. This is an Unsupervised Machine Learning problem, as we are not predicting any outcomes here.
• Reinforcement Learning: It’s a type of machine learning where an agent learns to make decisions by interacting with an environment. It receives rewards or penalties based on its actions, gradually improving its strategy to maximize cumulative rewards over time. It is a slightly complex topic as compared to traditional machine learning, but an equally crucial one for the future. This article provides a good introduction to reinforcement learning.

What Steps Are Involved in Building Machine Learning Models?

Any machine learning model development can broadly be divided into six steps:

• Problem definition involves converting a business problem to a machine learning problem
• Hypothesis generation is the process of creating a possible business hypothesis and potential features for the model
• Data Collection requires you to collect the data for testing your hypothesis and building the model
• Data Exploration and cleaning help you remove outliers, missing values, and then transform the data into the required format.
• Modeling is when you finally build the ML models.
• Once built, you will deploy the models

Why Is Machine Learning Getting So Much Attention Recently?

The obvious question is, why is this happening now when machine learning has been around for several decades?

This development is driven by a few underlying forces:

1. The amount of data generation is significantly increasing with the reduction in the cost of sensors

2. The cost of storing this data has reduced significantly

3. The cost of computing has come down significantly

4. Cloud has democratized computing for the masses

These 4 forces combine to create a world where we are not only creating more data, but we can store it cheaply and run huge computations on it. This was not possible before, even though machine learning techniques and algorithms were already there.

What tools are used in Machine Learning?

There are several tools and languages being used in machine learning. The exact choice of the tool depends on your needs and the scale of your operations. But here are the most commonly used tools:

Languages:

• R – Language used for statistical computing, data visualization, and data analysis.
• Python – Popular general-purpose language with strong libraries for data science, machine learning, and automation.
• SAS – Proprietary analytics software suite widely used in enterprise environments for advanced analytics and predictive modeling.
• Julia – A high-performance programming language designed for numerical and scientific computing.
• Scala – A Functional and object-oriented programming language that runs on the JVM, often used with Apache Spark for big data processing.

Databases:

• SQL – Structured Query Language used to manage and query relational databases.
• Hadoop – Open-source framework for distributed storage and processing of large datasets using the MapReduce programming model.

Visualization tools:

• D3.js – JavaScript library for producing interactive, data-driven visualizations in web browsers.
• Tableau – Business intelligence tool for creating dashboards and interactive visual analytics.
• QlikView – A Data discovery and visualization tool with associative data modeling for business analytics.

Other tools commonly used:

• Excel – Widely used spreadsheet software for data entry, analysis, modeling, and visualization in business environments.

Check out the articles below elaborating on a few of these popular tools (these are great for making your ultimate choice!):

• 6 Useful Programming Languages for Data Science You Should Learn (that are not R and Python)
• Python vs. R vs. SAS – which tool should I learn for Data Science?

How is Machine Learning Different from Deep Learning?

Deep learning is a subfield of Machine Learning. So, if you were to represent their relation via a simple Venn diagram, it would look like this:

You can read this article for a detailed deep dive into the differences between deep learning and machine learning.

What are the different algorithms used in Machine Learning?

The algorithms in machine learning fall under different categories.

• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • K-nearest Neighbors
  • Decision Trees
  • Random Forest
• Unsupervised Learning
  • K-means Clustering
  • Hierarchical Clustering
  • Neural Network

For a high-level understanding of these algorithms, you can watch this video:

To know more about these algorithms, along with their codes, you can look at this article:

• Commonly Used ML Algorithms (with Python and R Codes)

Data in Machine Learning

Everything that you see, hear, and do is data. All you need is to capture that in the right manner.

Data is omnipresent these days. From logs on websites and smartphones to health devices, we are in a constant process of creating data. 90% of the data in this universe has been created in the last 18 months.

How much data is required to train a machine learning model?

There is no simple answer to this question. It depends on the problem you are trying to solve, the cost of collecting incremental data, and the benefits coming from the data. To simplify data understanding in machine learning, here are some guidelines:

• In general, you would want to collect as much data as possible. If the cost of collecting the data is not very high, this ends up working fine.
• If the cost of capturing the data is high, then you would need to do a cost-benefit analysis based on the expected benefits coming from machine learning models.
• The data being captured should be representative of the behavior/environment you expect the model to work on

What kind of data is required to train a machine learning model?

Data can broadly be classified into two types:

1. Structured Data: Structured data typically refers to data stored in a tabular format in databases in organizations. This includes data about customers, interactions with them, and several other attributes, which flow through the IT infrastructure of Enterprises.
2. Unstructured Data: Unstructured Data includes all the data that gets captured, but is not stored in the form of tables in enterprises. For example, letters of communication from customers or tweets and pictures from customers. It also includes images and voice records.

Machine Learning models can work on both Structured as well as Unstructured Data. However, you need to convert unstructured data to structured data first.

Applications of Machine Learning in Day-to-Day Life

Now that you get the hang of it, you might be asking what other applications of machine learning are and how they affect our lives. Unless you have been living under a rock, your life is already heavily impacted by machine learning.

Let us look at a few examples where we use the outcome of machine learning already:

• Smartphones detect faces while taking photos or unlocking themselves
• Facebook, LinkedIn, or any other social media site recommending your friends and ads that you might be interested in
• Amazon recommends products based on your browsing history
• Banks using Machine Learning to detect fraudulent transactions in real-time

Read more: Popular Machine Learning Applications and Use Cases in Our Daily Life

What are some of the Challenges to Machine Learning?

While machine learning has made tremendous progress in the last few years, there are some big challenges that still need to be solved. It is an area of active research, and I expect a lot of effort to solve these problems shortly.

• Huge data required: It takes a huge amount of data to train a model today. For example, if you want to classify Cats vs. Dogs based on images (and you don’t use an existing model), you would need the model to be trained on thousands of images. Compare that to a human – we typically explain the difference between a Cat and a Dog to a child by using 2 or 3 photos.
• High compute required: As of now, machine learning and deep learning models require huge computations to achieve simple tasks (simple according to humans). This is why the use of special hardware, including GPUs and TPUs, is required.
• Interpretation of models is difficult at times: Some modeling techniques can give us high accuracy, but are difficult to explain. This can leave the business owners frustrated. Imagine being a bank, but you cannot tell why you declined a loan for a customer!
• More Data Scientists needed: Further, since the domain has grown so quickly, there aren’t many people with the skill sets required to solve the vast variety of problems. This is expected to remain so for the next few years. So, if you are thinking about building a career in machine learning, you are in good standing!

Final Words

Machine learning is at the crux of the AI revolution that’s taking over the world by storm. Making it even more necessary for one to know about it and explore its capabilities. While it may not be the silver bullet for all our problems, it offers a promising framework for the future. Currently, we are witnessing the tussle between AI advancements and ethical gatekeeping that’s being done to keep it in check. With ever-increasing adoption of the technology, it’s easy for one to overlook its dangers over its utility, a grave mistake of the past. But one thing for certain is the promising outlook for the future.