A Concise Introduction to Machine Learning

When machine learning started gaining traction, many were afraid that AI robots would take their jobs.

Others started voicing concerns about the dangers of autonomous weapons and AI becoming the dominant form of intelligence. We have many sci-fi movies to thank for such concerns, but science fiction has indeed started colliding with reality.

Although we’re nowhere near having sentient androids, we’re getting closer to artificial general intelligence (AGI).

And that’s all thanks to machine learning.

What Is Machine Learning?

Machine learning is a subset of artificial intelligence. AI is a computer science that focuses on developing machines that can perform cognitive tasks like humans.

ML helps AI-powered machines “think” like humans and learn from all the data they collect and analyze. It helps them learn from experience to train themselves autonomously, without human supervision or explicit programming.

It’s the fundamental piece in an AI puzzle that enables AI systems to evolve on their own.

How Is Machine Learning Different Than AI?

AI is necessary for designing intelligent machines that can simulate human behavior and cognitive processes. ML is the application of AI that enables those machines to learn from data.

To better understand it, picture a Venn diagram, where ML is a small part of an AI circle.

That means that all ML systems and algorithms count as AI, but not all AI systems are based on machine learning.

What About Deep Learning?

There’s a lot of confusion between machine learning and deep learning, as they’re very closely related.

That Venn diagram you’ve just pictured? Deep learning would be a part of the ML circle.

It’s a subset of machine learning that focuses on artificial neural networks (ANNs) that enable ML algorithms to “think” and learn like humans and solve complex problems.

If ML uses algorithms to sift through and learn from data, deep learning is what structures them in multiple layers to build an ANN that can learn on its own and make intelligent decisions.

Types of Machine Learning

Depending on the nature of learning, there are three main types of machine learning:

• Supervised learning
• Unsupervised learning
• Reinforcement learning

Supervised Learning

Supervised learning involves giving an example model of inputs and outputs to an algorithm so that it can solve a future problem using general rules.

For instance, if you feed historical data to an algorithm, it can detect patterns to predict possible future outcomes with accuracy.

Depending on the learning problems, supervised learning has two sub-categories:

• Classification
• Regression

Classification algorithms predict discrete output values, that is, class labels, such as “Spam” or “Not Spam,” and “True” or “False.”

Regression algorithms predict continuous output values, that is, numerical labels, such as age and prices.

Unsupervised Learning

In unsupervised learning, an ML algorithm has to determine patterns in data without previous input-output examples.

It has to deduct general rules on its own and predict correct output for all the problems.

That’s very useful for driving insights from complex data and making informed, meaningful decisions. It also helps you feed new input values to supervised learning algorithms.

Unsupervised learning also has two sub-categories:

• Clustering
• Association

Clustering involves dividing inputs into groups. Contrary to classification, the groups and labels are unknown, so an algorithm must recognize and divide them independently.

For instance, it can analyze customer behavior to segment them and improve recommendation systems and targeted marketing.

The association process involves uncovering connections between different variables in a large data set to reduce dimensionality and drive meaningful insights.

For instance, you can learn that customers who buy a new bed are likely to buy a mattress as well.

Reinforcement Learning

In reinforcement learning, an ML algorithm learns from experience, without supervision or previous labels. However, it differs from unsupervised learning in that it includes rewards and penalties.

Video games are a great example of reinforcement learning algorithms. A user observes the environment, chooses an action, and receives a reward for doing something correctly, or gets a penalty for making a mistake.

Just like humans learn from trial and error, reinforcement learning algorithms learn and update their knowledge using feedback. That’s similar to supervised learning, but the algorithms use feedback to make real-time decisions.

Real-World Applications of Machine Learning

Machine learning has a wealth of applications across industries.

Some of the most popular Machine Learning-based tools include various retail industry solutions. They can predict customer preferences, recommend products, forecast sales, locate targeted merchandise, optimize pickup and delivery routes, and more.

ML algorithms can also help with CLV (Customer Lifetime Value) modeling, dynamic pricing, customer segmentation, personalization, targeted marketing, demand and supply management, customer support, etc.

HR professionals are using ML systems to sift through resumes faster to find the right candidates, screen them without human bias, and hire top talent.

Machine learning also helps filter email spam and malware, predict stock market trends, detect and prevent fraudulent transactions, and predict real-time traffic conditions.

It has numerous applications in healthcare, as well. It can help with disease predictions, drug discovery and https://thehealthchoices.com development, medical diagnostics, medical imaging diagnosis, clinical research, and more.

One of the most innovative applications of machine learning has to do with autonomous vehicles. Self-driving cars wouldn’t be able to operate without ML. It enables them to learn from the collected data on the surroundings to navigate safely and avoid people and objects successfully.

Virtual assistants, such as Amazon Alexa, Siri, Cortana, and Google Assistant, also rely on ML algorithms. They use speed recognition to record voice commands before putting ML algorithms to work to analyze the commands and respond accordingly.