Understanding what is Machine Learning: Basics, Pros, and Cons

Machine learning is the concept of enabling computers to learn from a provided set of data and make decisions without being instructed to. Computers analyze patterns, algorithms and statistics to make decisions on their own. Also, according to Statista, the market size in machine learning is anticipated to grow by 34.80% annually from 2025 to 2030, reaching a market volume of US$503.40 billion by 2030.


Now, note that artificial intelligence and machine learning is not the same thing. In fact, machine learning is a branch of artificial intelligence that was introduced to make things easy for us, since it doesn’t rely on constant instructions to do tasks.  

Want to know what machine learning is all about? Follow below. 

The Basics of Machine Learning 

Now that we are clear on the concept of machine learning and how it differs from AI, let’s concrete our understanding further by understanding the basics of machine learning.  

Here are the 4 basics of machine learning that you need to know: 

• Key Concepts 
• Types of Machine Learning 
• Machine learning Workflow 
• Common Algorithms 

Key Concept 

The key concepts of machine learning involve: 

• Data: The data that you provide your computer is what your computer eventually uses to make decisions. It can be either structured (e.g. tables) or unstructured (e.g. text). 
• Model: Now, your provided data will be processed mathematically to make predictions. For this many types of models are used: like, linear regression, decision trees and neural networks.  
• Training: this is the process of teaching models to analyze and recognize the patterns in the data that you have provided.  
• Testing: This is done on unseen data right after your model has been trained and before executing it to real world applications. The purpose of testing is to ensure that your trained model is performing well or as expected.  
• Features: this refers to the special qualities in your data provided. 

Types of Machine Learning 

• Supervised Learning: Here the models are provided labelled data to learn how to make predictions. This means that each data point has a known output value. This helps the algorithm to understand the relationship between input features and the wanted outputs.  

A real-world example of supervised learning could be spam email detection. The model is provided with two categories of email: spam and not spam. The model can identify spam emails based on certain words or the structure of email.  

• Unsupervised Learning: In this learning, the model is not provided with any labelled data. The algorithm of the model has to analyze and recognize certain patterns to make predictions. 

For example, text clustering. In this, the model has to categorize different documents. They can analyze it and then categorize similar documents based on their content, text and keywords. 

• Reinforcement Learning: Here the model is trained to make decisions through a reward and punishment system.  

For example, in self-driving cars, the cars learn to drive by receiving feedback from their environments in the form of rewards and penalties.  

Machine Learning Workflow 

• Data collection: Collect raw data that seems to be suitable for the problem 
• Data processing: This involves formatting, cleaning and transforming the data, like handling missing values.  
• Feature selection: select features from data that are related to the problem. 
• Model: Choose the most suitable model for your task. 
• Training: Train your model based on the given dataset. 
• Evaluations: Test your model on unseen data to ensure good performance.  
• Deployment: Model is ready to be used for real world scenarios.  

Common Algorithms 

Here is a list of algorithms in machine learning: 

• Linear Regression 
• Logistic Regression 
• Decision Trees 
• K-Means Clustering 
• Neural Networks 

Machine Learning Pros & Cons 

Advantages of Machine Learning 

• Automation of Repetitive Tasks: Machine learning can automate repetitive tasks. This can free human workers from extra workload and allow them to focus on more difficult tasks.  
• Improved Decision-Making: This is very helpful for businesses since machine learning can analyze large datasets and can recognize certain trends and patterns that humans cannot.  
• Personalization: With the help of machine learning algorithms, personalization has become an easier process. Since these algorithms can recommend users, products based on their preferences and choices.  
• Enhanced Data Security: Machine learning is highly used in cybersecurity to detect any threats or abnormalities. By analyzing certain patterns and unusual activities, machine learning is able to protect any sensitive data from harm. 

Drawbacks of Machine Learning 

• Data Dependency: The greatest drawback of machine learning is that it demands sufficient and high-quality data to make decisions and perform well. Hence, less data can often lead to inaccurate models and wrong predictions.  
• High Costs and Resources: Developing and maintaining machine learning is very costly as it requires a lot of resources.  
• Lack of Interpretability: Often machine learning models make use of “black boxes” to reach their decisions. This lack of transparency can result in confusion, especially in sectors like healthcare and finance where understanding the reasoning is very important.  
• Ethical Concerns: Like every technology there have been ethical concerns raised regarding machine learning. For example, if the training data is biased then biased predictions will be made.  

Real-World Machine Learning Applications  

• Healthcare: Machine learning is playing an important role in the healthcare sector by predicting patient outcomes and making them a personalized treatment plan. (Built In- AI in healthcare) 
• Finance: In finance, machine learning is helping to predict fraud, risks algorithmic trading, and customer service automation 
• Retail: Machine learning allows retailers to understand consumer behavior, keep inventory optimized and enhance customer experience. (Forbes- AI in retail) 
• Transportation: With the help of machine learning now we get to have autonomous vehicles, route optimizations and predictive maintenance.  

See? Machine learning is truly reshaping industries for the better, especially for businesses because it automates tasks and provides an enhanced customer experience. Platforms like CommCart are taking advantage of machine learning to provide powerful e-commerce tools and bring efficiency.  

Great! Now we have an in-depth knowledge of machine learning; from its advantages and disadvantages to its uses. There is no doubt that machine learning has made our lives easier with its ability to make predictions and decisions without any instructions.  

Just make sure that you are well aware of the basics of machine learning along with its pros and cons to use it the right way since like every technology machine learning also comes with its challenges.  

Want a smooth pathway in machine learning without having to encounter any challenges? Then stay tuned because we’ll be sharing some crucial ways to overcome challenges in machine learning! For any further information or query contact us now!