Machine Learning and Deep Learning: Types, Similarities and Differences

Learning is no longer exclusive to humans; with the advent of Artificial Intelligence (AI), the computing world has dramatically changed from a mundane mechanical system into a robust automated one. In its simplest form, AI aims to simulate human intelligence into machines for better decision-making.

We can broadly categorize AI as weak or strong. The former consists of systems designed and trained to perform a particular task. They can obey program commands but cannot work without human intervention.

On the other hand, strong AI refers to generalized human cognitive ability. It doesn’t require human intervention to perform tasks and find solutions.

Although true AI is still decades away, a particular type of AI known as “Machine Learning powered AI” is now available.

The AI is forked into two cognate techniques – Machine Learning AI and Deep Learning AI. The latter is actually a subset of the first one, but due to its larger impact, we study it separately.

Both have diverse applications in various sectors, but their absolute powers are yet to be divulged.

In this article, I have provided an overview of Machine Learning and Deep Learning and explained the theory behind them and their applications.

What is Machine Learning?

Until recently, computers could do only what they were programmed to do. Machine Learning is a technique that enables computers to mimic human beings. It was invented by Arthur Samuel, the pioneer of AI and computer gaming, in 1952 when he designed the first computer program to learn as it was executed.

Machine Learning is a subset of AI that allows the software to learn from past experience and use that knowledge to improve its future performance without being explicitly programmed.

Data is the fuel that drives the learning process because machines learn from the data provided to them. Machine Learning requires colossal data resources and robust computational power that have only recently become available to us.

A brief history of Machine Learning

The foundations of Machine Learning were laid in 1943 when Warren McCulloch, a neurophysiologist, along with a mathematician named Walter Pitts, authored a paper on neurons and their working (read the paper here). The two created a model with electrical circuits, bringing the first neural network to life.

The famous “Turing Test” was created in 1950 by Alan Turing to ascertain whether computers had real intelligence. In 1952, Arthur Samuel developed the first computer program that could learn as it played a game of checkers. In 1957, Frank Rosenblatt designed the first neural network, the “perceptron.”

In the 1990s, a significant shift happened where Machine Learning moved from being knowledge-driven to a data-driven technique due to the availability of large volumes of data. In 1997, IBM’s Deep Blue became the first machine to defeat the world champion in a game of chess. Businesses recognized that complex calculations could be carried out more efficiently through Machine Learning.

Some prominent examples of Machine Learning in recent times include Google Brain and Deep Fake

In 2015, Elon Musk, Ilya Sutskever, Greg Brockman, Trevor Blackwell, Vicki Cheung, and others created an organization named OpenAI to develop safe and friendly AI that could benefit humanity. OpenAI has been relentlessly making efforts in Machine Learning and AI to bring new innovations to the field.

Newer Machine Learning breakthroughs include computer vision, natural language processing, reinforcement learning and, obviously, Deep Learning.

Traditional Programming vs. Machine Learning

In conventional programming, a computer program uses the data and runs on a computer system to generate the output. On the contrary, in Machine Learning, the data and the outputs (also known as labels) are provided as the input to an algorithm that comes up with a model as an output.  

For example, if we feed the customer demographics and transactions as input data and use the past customer churn rates as the output data. In that case, an algorithm can construct a model that predicts whether a customer will churn. Such a model is known as a predictive model.

We can use these Machine Learning models to predict any situation with the necessary historical data. Machine Learning techniques are valuable because they allow computers to learn new rules in a high-dimensional complex space that is hard to comprehend by humans.

What is the need for Machine Learning?

Machine Learning has allowed us to apply mathematical calculations automatically and quickly to huge volumes of data. We can use it to automate many tasks, especially the ones that only humans can perform with their innate intelligence until now. Thus, Machine Learning has widespread applications such as self-driving cars, online recommendation engines, cybercrime detection, image and speech recognition, language translation, and sales forecasting.

Operations such as decision-making, forecasting, making predictions, providing alerts on deviations, and uncovering hidden trends require diverse, real-time data from different artifacts that the Machine Learning paradigm could best handle.

Features of Machine Learning

Today, almost every business is willing to embrace Machine Learning because of the numerous benefits involved. Some key features of Machine Learning include:

• Automation – Machine Learning (ML) can automate repetitive tasks and increase business productivity.
• Improved customer engagement – ML helps businesses provide a customized experience and excellent customer service, promoting brand loyalty and maintaining long-standing customer relationships.
• Automated data visualization – Platforms such as Auto Viz provide data visualizations to identify relationships between the enormous volumes of data generated by businesses, machines and individuals to enhance business productivity.
• Accurate data analysis – Machine Learning offers efficient algorithms and data-driven models to handle real-time data.
• Business intelligence – When combined with big data analytics, Machine Learning features could help businesses find solutions to problems and help them grow and make more profit.

Languages for Machine Learning

There are various programming languages available for Machine Learning. The language choice and the required programming level depend on how we use Machine Learning in an application. Thus, there is no single “best” programming language for Machine Learning.

The five most popular languages for ML programming are:

• Python
• R
• Java
• Julia
• LISP

Types of Machine Learning

These are some different types of Machine Learning and their applications.

• Supervised learning – It is the class of problems that uses a model to understand the mapping between the input variables and the target variable.
• Unsupervised learning – Here, the model tries to learn by itself, recognize patterns, and extract the relationships among the data.
• Reinforcement learning – It is a type of problem where an agent operates in an environment based on the feedback or reward provided by the environment in which it operates.

Machine Learning algorithms

Machine Learning algorithms can be grouped based on their learning pattern and the similarities in their functions.

Based on their learning style:

• Supervised learning algorithms
• Unsupervised learning algorithms
• Semi-supervised learning algorithms

Based on the similarity of their functions:

• Regression algorithms
• Instance-based algorithms
• Regularization
• Decision tree algorithms
• Bayesian algorithms
• Clustering algorithms
• Association rule learning algorithms
• Artificial neural network algorithms
• Deep Learning algorithms
• Dimensionality reduction algorithms
• Ensemble algorithms

Machine Learning life cycle

The Machine Learning process comprises of several stages to design, develop and deploy high-quality models. Its life cycle consists of the following seven steps:

1. Data collection – This step aims to identify and gather all the relevant data to the problem. The efficiency of the output depends directly on the quality of the data gathered.
2. Data preparation – The collected data are organized and put in one place for further processing. It includes data exploration, where the data's characteristics, nature, format and quality are accessed.
3. Data wrangling – The raw data is cleaned and converted into a useful format.
4. Data analysis – This step deals with the feature and model selection processes. Only variables beneficial to the model are chosen. After that, an appropriate Machine Learning technique is selected, and the model is built using the data.
5. Model training – The model is trained on the training data until its performance reaches an acceptable level.
6. Model testing – After training, the model is tested to evaluate its performance on the unseen test data.
7. Deployment – Finally, we deploy the model constructed in the real-world system.

Deep Learning

Deep Learning is a subset of Machine Learning that follows the functionality of neurons in the human brain. The Deep Learning network consists of multiple neurons interconnected with each other in layers. Processing occurs through connections containing the input data, pre-assigned weights and the activation function.

The network operates on large volumes of data and propagates them through each layer by learning complex features at each level. If the model's outcome is not as expected, the weights are adjusted, and the process is repeated until the desired outcome is achieved.

The learning process in a neural network occurs in two steps. A nonlinear transformation is applied to the input during the first step, and a statistical model is created. In the second step, the model is improved using a mathematical model known as the derivative. These two are continuously repeated (known as “iteration”) until it reaches the desired level of accuracy.

Types of neural networks

Today, different neural networks are available for various processes. The most commonly used ones are:

• Perceptron – A single-layered neural network that contains only an input layer and an output layer.
• Feedforward—The simplest form of neural network where the information flows only in one direction.
• Recurrent neural network – It saves the network’s output in its memory and feeds it back to the network to help predict the output.
• Convolutional neural network – It extracts valuable information from unstructured data such as signal and image processing applications.

Reinforcement learning – Here, the agent operates in a complex and uncertain environment and learns through trial and error. It is rewarded or punished virtually for its actions and learns to maximize the rewards, refining the output produced.

Differences between Machine Learning and Deep Learning

This table can illustrate the salient differences between Machine Learning and Deep Learning.

#	Machine Learning	Deep Learning
1	It is a superset of Deep Learning.	It is a subset of Machine Learning.
2.	The data representation uses structured data.	The data representation uses neural networks.
3.	It is an evolution of AI.	It is essentially an evolution of Machine Learning.
4.	It consists of thousands of data points.	It consists of millions of data points.
5.	Its output is in the form of numerical values.	Its output can be anything from numerical values to free-form elements such as free text and sound.
6.	It uses various automated algorithms that turn to model functions and predicts future action from data.	It uses a neural network that passes data through processing layers to interpret data features and relations.
7.	Data analysts detect algorithms to examine specific variables in data sets.	Algorithms are mostly self-depicted on data analysis after they’re put into production.
8.	It is used to stay in the competition and learn new things.	It solves complex machine-learning issues.
9.	Training can be performed using the CPU.	A dedicated GPU is needed for training.
10.	More human intervention is required.	Less human intervention is required.
11.	Its model is small-sized and takes less time to train.	Training takes a significant amount of time due to massive data points.

Conclusion

In the future, both Machine Learning and Deep Learning will play fundamental roles in all major domains, such as health, business, environment, public safety, and security. By learning about the applications of Machine Learning and Deep Learning, you will have a clearer idea about AI's current and future capabilities. I hope this guide helps you get acquainted with all the salient aspects of these two important subsets of AI.