Introduction to Machine Learning and its applications — Beginner’s Guide


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Machine Learning is a subfield of Artificial Intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.

We can teach machines how to learn and some machines can even learn on their own, this is phenomenon is called Machine Learning.

Machine Learning feeds a computer data and uses statistical techniques to help it “learn” how to get better at a task — without having been specifically programmed for that and eliminating the need for millions of lines of written code. The machine gets smarter over time. You continually feed it with data, and it continually analyses and absorbs. As the computer gains experience, it improves its performance and becomes more intelligent.

Machines can predict the future, as long as the future doesn’t look too different from the past.

Machine Learning is about:

Creating algorithms (a set of rules) that learn from complex functions (patterns) from data to make predictions on it. Essentially, it can be summarized in 3 Steps:

  • It takes some data
  • Finds a pattern from the data
  • Predicts new pattern from the data

How Do Machines Learn?

The way the machine learns is similar to human beings. Humans learn from their experiences. Machines “learn” by finding patterns in similar data. The machine learns through the discovery of patterns. This discovery is made thanks to the data.

Think of data as information regarding anything, the more the information more it learns. The more data given to a machine, the “smarter” it gets. But not all data are the same. Imagine if the machine is in the wrong hands, wrong information being fed to the machine can be dangerous.

Everything is dependent on data, this information can either lead you in the right direction or the wrong direction. The better the data that is obtained, the more uncertainty is reduced. So it’s important to keep in mind the type of data you’re giving to your machine to learn.

The Machine Learning programs is straightforward and can be summarized in the following points:

  1. Define a question
  2. Collect data
  3. Visualize data
  4. Train algorithm
  5. Test the Algorithm
  6. Collect feedback
  7. Refine the algorithm
  8. Loop 4-7 until the results are satisfying
  9. Use the model to make a prediction

After a sufficient amount of data is given, the machine can make predictions. Machine “learns” really by using old data to get information about what will happen.

Types of Machine Learning:

Supervised Machine Learning

The machine learns from labeled data. Normally, the data is labeled by humans. Supervised learning is the most common type of learning because it is easier to train a machine to learn with labeled data than with un-labeled data. Supervised Machine Learning is great for recurring problems. The ones that we’ve already solved once, but that come up time and again.

Depending on what you want to predict, supervised Machine Learning can be used to solve two types of problems:


If you want to predict continuous values, such as trying to predict the weather outside in degrees, you would use regression. This type of problem doesn’t have a specific value constraint because the value could be any number with no limits.


If you want to predict discrete values, such as classifying something into categories, you would use classification. A problem like, “Will he buy it?” will have an answer that falls into two specific categories: yes or no.

Unsupervised Machine Learning

The machine learns from un-labeled data. Meaning, there is no “right” answer given to the machine to learn. Unsupervised Machine Learning is to detect patterns in the data and to group them. Unsupervised learning is when machines trying to learn “on their own”, without help. Unsupervised Machine Learning is harder to implement. However, unsupervised Machine Learning opens the way to finding solutions to problems that humans can’t solve.

Unsupervised Machine Learning can group data together by:

Clustering Problem

Unsupervised learning tries to solve this problem by looking for similarities in the data. If there is a common cluster i.e. group, the algorithm would then categorize them in a certain form.

Association Problem

Unsupervised learning tries to solve this problem by trying to understand the rules and meanings behind different groups.

For example — stores may want to know what type of products were purchased together and use this information to organize the placement of these products for easier access. One store found out that there was a strong association between customers buying beer and diapers. They deduced from this statement that males who had gone out to buy diapers for their babies also tend to buy beer as well.

Reinforcement Machine Learning

The machine learns through a reward-based system. Reinforcement Machine Learning uses a reward system and trial-and-error in order to maximize the long-term reward. The goal is to reward the machine when it learns correctly and to penalize the machine when it learns incorrectly.
With the wide range of possible answers from the data, the process of this type of learning is a repetitive step. It continuously learns.

For Example:

  • Training a machine to learn how to play chess.
  • Self-driving cars

Applications of Machine Learning:

Medical diagnosis

Machine Learning provides methods, techniques, and tools that can help in solving diagnostic problems in a variety of medical fields. Machine Learning improves the accuracy of medical diagnosis by analyzing data of patients.

It is being used for the analysis of prediction of disease progression, for therapy planning and support, and for overall patient management. It can help in providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.

Financial services

Banks and other businesses in the financial industry use Machine Learning technology to identify important insights in data and to prevent fraud. The technology is also used to identify opportunities for investments and help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.


Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple data sources that can be mined for patterns and insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft.

Marketing and Sales

Companies are using Machine Learning technology to understand the purchase history of their customers and make personalized product recommendations by making these recommendations the company is able to promote their new product to the consumers for their next purchase. This ability to save, analyze, and use customer data to provide a personalized shopping experience is the future of sales and marketing which will definitely help in increasing the profit of the company.

Self-driving cars

Machine Learning plays an important role in self-driving cars. Tesla, the car manufacturing company has launched self-driving cars already, other companies like Mercedes, BMW, and Audi are also working on self-driving cars. It is using an unsupervised learning method to train the car models to detect people and objects while driving with the help of sensors and cameras around the car.

Social Media Services

Social media platforms are utilizing Machine Learning for their own and user benefits. Here are a few examples

  • People You May Know — Facebook notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone, etc. On the basis of continuous learning, a list of Facebook users are suggested that you can become friends with.
  • Face Recognition — You upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook checks the projections in the picture, notice the unique features, and then match them with the people in your friend list.

Search Engine Result Refining

Google and other search engines use Machine Learning to improve search results. Every time you do a search, the algorithms at the backend keep a watch at how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the results it displayed were in accordance, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirement. This way, the algorithms working at the backend improve the search results.

Online Fraud Detection

Machine Learning is making our online transactions safe and secure by detecting fraud transactions. For each genuine transaction, the output is converted into some hash values, and these values become the input for the next round. For each genuine transaction, there is a specific pattern that gets change for the fraud transaction hence, it detects it and makes our online transactions more secure.

Virtual Personal Assistant

We have various virtual personal assistants such as Google Assistant, Alexa, Cortana, Siri. These virtual assistants use Machine Learning algorithms as an important part. These assistants record our voice instructions, send it over the server on a cloud, and decode it using ML algorithms and act accordingly.

Machine Learning can theoretically solve any problem in almost any industry. Tech companies are using Machine Learning to improve user experience with personalizing recommendations.

Machine Learning is the FUTURE!

Vishak is a skilled Editor-in-chief at Code and Hack with a passion for AI and coding. He has a deep understanding of the latest trends and advancements in the fields of AI and Coding. He creates engaging and informative content on various topics related to AI, including machine learning, natural language processing, and coding. He stays up to date with the latest news and breakthroughs in these areas and delivers insightful articles and blog posts that help his readers stay informed and engaged.

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