Machine Learning (ML): Understanding & Getting Started With Machine Learning

Understanding Machine Learning (ML): You might have noticed that machine learning (ML) is lately emerging as one of the most discussed topics.

Whenever you scroll through your news feed or browse the web, you see somebody talking about the advances in the field of artificial intelligence (AI), machine learning (ML), and its impact on humans. So, it is natural to wonder what all this buzz is about and how the technology applies to different areas of life.

In this guide, let us understand all you should know about machine learning and how you can get started with it.

What is Machine Learning?

Machine learning, often abbreviated to ML, is an application of artificial intelligence that enables systems to automatically learn and improve themselves without having to be programmed manually.

It focuses on making systems that can identify patterns from data, use historical data to learn, and make logical interpretations and decisions without any human intervention.

ML is made up of algorithms designed to teach computers how to perform certain tasks that humans do on a daily basis.

Types of Machine Learning

Based on the type of problem a machine learning model is expected to solve, it can be classified into two basic categories.

Supervised ML

In this type of ML, the algorithms use labeled data where the outputs of the input information are already known.

Here, the ML algorithm is fed the inputs and correct outputs that it compares with its predicted outputs to figure out the model’s accuracy. It then optimizes the parameters of the model to improve the accuracy.

This type of ML is often used in applications involving many data records or in automation. An example is an algorithm that finds credit card transactions likely to be fraudulent.

Unsupervised ML

Unsupervised machine learning is applied to data with no structured answer; the output for input is not pre-determined.

On the contrary, the algorithm needs to analyze the input and give a suitable decision. This type of ML works on transaction-based data, like identifying customer segments with similar attributes.

Some of the most popular methods used in unsupervised machine learning include k-means clustering, nearest-neighbor, singular value decomposition, and others.

Why Machine Learning is Important

Machine learning is recently getting more popular and vital across applications and industries because of the constantly increasing volumes and varieties of data, the availability of ultra-fast internet, and the rise of computational power.

These factors allow people to automatically build models that can analyze complex, massive data sets with speed and accuracy.

Machine learning can be applied to a range of use cases to cut down costs, mitigate risks, and improve the quality of life across areas like cybersecurity, product/service recommendations, and lifestyle.

With better access to computing power and data, machine learning becomes increasingly important and will be integrated into different facets of human life in the near future.

How Machine Learning Works?

There are four primary steps you must consider following when it comes to getting started with machine learning.

1. Select and prepare a data training set

Training data refers to information the machine learning application consumes to tune the model’s parameters. Sometimes, training data is labeled or tagged to give classifications or values that the model should predict.

Some other data can be unlabeled, meaning the model must fetch features and allocate clusters autonomously.

2. Choose an algorithm for the set

The machine learning algorithm you select depends on a few things:

• How much data the training set contains
• The type of problem you aim to solve using the ML model
• Whether the application is a classification or prediction and uses labeled or unlabeled training data

While labeled training data applications like classification and prediction use logistic regression, ordinary least square regression, and other algorithms, unlabeled data might require clustering algorithms like nearest neighbor or k-means.

3. Train the algorithm

This step involves tuning the parameters and variables of the model to predict the results more accurately. Training an ML algorithm is generally an iterative process that uses different optimization techniques based on the model selected.

These methods don’t require manual intervention; the machine can learn from the data provided without a human user’s direction.

4. Use and enhance the model

The final step is to feed data into the created model to improve its accuracy and effectiveness. Based on the type of problem you are looking to solve, your new data can come from various sources.

For example, an ML model for self-driving cars will need to be fed real-world data about traffic, road conditions, and objects on the roads.

Real-Life Applications of Machine Learning

While ML technology has been around for years, it is only its recent advancements in mathematics and computations for big data that make it so powerful.

Today, the scope of machine learning applications is vast, ranging from online retail stores to enterprise AIOps. Here are some real-world examples of the applications of ML in use today:

• Digital assistants like Google Assistant, Alexa, and Siri look up on the web for information in response to voice commands
• Self-driving vehicle projects like Autopilot from Tesla and Waymo
• Use of behavioral analytics for cybersecurity to identify any suspicious activities or threats
• Algorithms based on ML give tailored recommendations on websites and applications like YouTube, Netflix, etc
• Spam filters on email boxes that spot and block unwanted mail
• ML-based image recognition can be relied upon for traffic monitoring, face recognition, handwriting recognition, and people counting
• Fraud detection systems collect data from multiple sources to identify patterns of suspicious activity. Such solutions use supervised as well as unsupervised ML to categorize financial transactions as legitimate or fraudulent
• Wearable devices that capture real-time data for use in continuous assessment of patient health

Final Thoughts

As data volumes continue to grow, internet bandwidth keeps increasing, computing power gets stronger, and data scientists improve their expertise, machine learning will only keep getting bigger and continue driving better efficiency across different aspects of life.

With the ever-changing landscape of cybersecurity, machine learning has become a valuable tool in securing data and keeping attackers away.