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A Guide To Machine Learning

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In today’s constantly advancing world of technology, machine learning is one concept that always comes up when discussing AI. Moreover, both of these terms are often used interchangeably. Where everybody uses machine learning and AI in various forms, what’s behind this dramatic field, and what impact have they left on human life? 

In this blog, you’ll discover all about machine learning, from the basics to the future, and its incorporation with AI!

What is Machine Learning?

Machine learning makes the machine learn or inherit human cognitive abilities and behavior. It lets the machine predict future trends, act, or decide based on past data and information. Depending on the purpose, there are different types of training in a machine-learning model based on the presence or absence of human influence on the raw data.

Types of Machine Learning:

On the basis of what kind of data is used to train the ML model, machine learning is of four types. Let us read about them to get more insights about ML. 

Supervised Learning: A set of pre-labeled data, labeled by the humans, is put into the machine to help it recognize the outputs accurately. This type of learning lets the machine give correct outputs using experience. For instance, just like we teach a child the names of the fruits, a machine is fed the fruit images with their names labeled so that whenever any fruit is input into the ML model, it can recognize its name and provide the correct output.

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Whenever data is input into the machine, it modifies its weight to fit onto the data appropriately. Overfitting means the machine fits the data so closely that it has learned its signals and noises (irrelevant information) rather than truly understanding the underlying patterns. On the other hand, underfitting is when the machine doesn’t fit the data closely enough. An under-fitted model can’t spot the relationship between the input and output variables accurately.  

Unsupervised Learning: As the name says, “Unsupervised,” unsupervised ML recognizes underlying, hidden patterns and data groupings without human supervision or interference. In this type of learning, the ML model learns through unlabeled data and can analyze and group the unlabeled datasets (called clusters). This is useful when the users are unsure of the similarities between the data sets. For instance, when a group of fruit images (unlabeled) is provided to an unsupervised model, it will automatically observe the differences in color, shape, and size and classify different fruits into different groups. 

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Semi-Supervised Learning: Semi-supervised learning is a medium between supervised and unsupervised learning. It is when a small amount of labeled data is used to guide the machine about classification and feature extraction from many unlabeled datasets. It is often used when a sufficient amount of labeled data is unavailable or it is costly to label. Below is an example of semi-supervised learning where the model is provided with partially labeled data of three fruit images- apple, orange, and banana. Out of the three fruits, only banana and orange are labeled, so when an unlabelled fruit (apple) is input into the ML model, it will first classify it as not orange and not banana, and then, based on the training, the model will label it as an apple. 

 

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Reinforcement Learning: Remember the “Hit and Trial” technique we used in Maths? Similar to it, reinforcement learning is when the algorithm uses the “Trial and Error” technique. It is similar to supervised learning, but the algorithm is not trained using sample data. Instead, it uses a series of successful outcomes or calculations to obtain accuracy and proficiency. When the algorithm does the right action and gets a reward in return, it is called “Positive Learning,” when it does a negative action, it will record the observation in its memory. This is called ‘Negative Learning.” Below is a diagram of An Agent (ML model) interacting with the environment and getting an observation or reward as the response. 

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Common Machine Learning Algorithms.

The following are the common algorithms used to train ML models- 

Linear Regression: Predictions are based on the linear relationship between the independent input variable and at least one target (dependent) variable. A linear relationship is defined when the data relationship between two variables forms a straight line. Using this, observations can be made whether a data point increases, decreases, or stays constant compared to the other independent variable. Linear regression machine learning models are used to map linear regression based on the dataset. This means when input and target variables are input in an ML model, they will map the coefficients that best fit the line, and hence, they attempt to map a straight line through the data set. 

Logistic Regression: It is a supervised learning algorithm for classifying the data. Data machine learning models use logistic regression to generate categorical responses like “Yes or No.” Instead of developing a continuous output like linear regression, it predicts the probability of occurrence of a binary event. For instance, a logistic regression model for email spam detection will indicate if an email is spam or not. When given a data set, the logistic regression model will calculate weights and biases, further categorizing the dataset. 

Decision Trees: Decision trees are one of the most powerful algorithms of supervised learning that are used to predict numerical values (linear regression) and categorize the data (logistic regression). Flowcharts like tree diagrams test or organize data according to some categorizing schema. They are advantageous as the tree-like diagram makes it easy to understand, visualize, and validate the data. 

Random Forests: Random forests aggregate the results of several decision trees to predict a value or categorize the data. They improve the precision and efficiency of individual trees and can be used for regression and classification. 

Neural Networks: AI algorithms that work like the human brain in processing and interpreting information. Just like neurons in our brain, neural networks have nodes called artificial neurons that carry forward information similar to human neurons. Each artificial neuron has some threshold and weight, and as soon as it reaches the threshold value, it gets activated and passes the data to the next artificial neuron. Neural networks are designed to learn quickly from the input sample data and improve their efficiency and accuracy with every use. Neural networks serve as the key examples of the power and potential of artificial intelligence, and they are widely used in various industries like finance, healthcare, automation, manufacturing, and others. 

Thus, all the types of machine learning and these algorithms together form the basics of Machine Learning. 

Apart from this, there are many Machine Learning Tools like Microsoft Azure Machine Learning, IBM Watson, Google TensorFlow, Amazon Machine Learning, OpenNN, PyTorch, BigML, and Apache Mahout, that help data scientists and developers bring these mathematical concepts from books to production and ML development. 

Real-Life Examples of Machine Learning

The feature of machine learning to make predictions based on the pre-recorded dataset is widely used in many day-to-day software and applications. The following are the most common applications of ML used in real life. 

  • Recommendation Systems: ML algorithms are widely used in recommendation systems of popular e-commerce websites like Amazon, music applications like Spotify, and video streaming platforms like Netflix. ML algorithms make predictions based on your browsing history, purchases, music history, watch history, and similar users and then show the outputs on your home pages or relevant apps to boost sales or catch your attention. 

 

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Social Media Connections: The “People you may know” list you usually see on your top social media platforms like Facebook and Instagram is another example of ML that predicts familiar people to you based on your current network, likes, comments, and contacts. 

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Automated Stock Trading: This feature is used wildly for finance, real estate, and product development. Stock investments are a key to growing your wealth. Predictive analytics and algorithmic trading can predict the future trends of the stocks. For this purpose, ML algorithms divide the data of the past trends of the stock market, group them, and define them according to the data analytics rules, using which analysts can take a course of action. This technique allows ML algorithms to pretend stock trends for after a year and beyond.

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  • Traffic Predictions: Whenever you navigate to a particular location through Google Maps, it automatically shows you the fastest route and estimated arrival time. Behind this is also a hidden ML algorithm. Google Maps collects traffic data, makes predictions based on it, and shows the best-suited route with the lowest traffic. 
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Credit Card Fraud Detection: Predictive analysis is used to measure the likeliness of a fraudulent activity. Data based on previous fraudulent acts is embedded in AI and ML models, and it detects the variables used in those cases. That data is then used to predict if the transaction is fraudulent or legitimate.

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  • Self-Driving Car Technology: AI, ML algorithms (most likely reinforcement learning), and deep learning techniques are used to guide self-driving cars to recognize the objects on the roads and interpret traffic signals and other people. Hence, artificial intelligence, machine learning, and deep learning control the car’s response in every situation.

 

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Machine Learning v/s AI.

People usually confuse artificial intelligence and machine learning and use them interchangeably. However, the confusion is understandable as both technologies are closely related and are often put into use together. 

So, what’s the difference between ML and AI?
To know the difference, one must know that artificial intelligence is a broader concept with a wide scope of applications. At the same time, machine learning is an application of AI with a limited scope of applications.  

AI refers to inheriting human behavior and cognitive abilities into machines. AI allows machines to think, see, understand, recognize, analyze, decide, or take actions like humans do. On the other hand, machine learning makes the machine learn from past data and information. It allows the machine to perform a particular task, recognize underlying patterns to analyze, and draw insights or predictions based on that data. ML models improve their efficiency and accuracy with time.

Benefits of using Machine Learning with AI.

As technology is growing at a vast pace, ML and AI can bring a lot of benefits to companies of all shapes and sizes together. With more data emerging every day, the need for enhanced, automated, and intelligent systems that can handle the complexity of data is also increasing. Due to this, ML and AI-embedded systems are being used for daily and varied operations. Following are some of the benefits of machine learning with AI: 

Increased Efficiency & Accuracy: Increase operational efficiency and accuracy without manual intervention, which in turn helps in reducing costs. 

Wider Data Ranges:  These intelligent systems can handle large amounts of data, including structured and unstructured data. 

Faster Decision-Making: Due to data-driven insights and facts, decision-making gets quicker and easier. 

Analytics Integration: Integrating predictive analysis into business reporting empowers employees and increases productivity. 

History And Evolution Of Machine Learning And Artificial Intelligence

The roots of machine learning and artificial intelligence, were followed by a knowledge-driven approach and shifted to a data-driven approach over time. Below is the Timeline of How Machine Learning evolved through the years:  

1943:  The history of Machine Learning starts. Walter Pitts and Warren McCulloch proposed the first scientific paper presenting the first mathematical model of neural networks. 

1950: Alan Turing conducted the Turing Test to measure a computer’s intelligence. He brought up the concept of “Can Machines Think?”

1952: Arthur Samuel wrote the first computer learning program for the game of checkers. The more the computer played games, the more it learned and improved.

1956: John McCarthy and Marvin Minsky proposed the first AI program at the Dartmouth Conference (known as the Founding Event of AI) and coined “Artificial Intelligence.” 

1957: Frank Rosenblatt designed the first neural network for computers, the algorithm that worked similarly to the human brain. 

1959: Arthur Samuel coined the term “Machine Learning.” A  synonym, self-learning computers, was also used back then. 

1967: The nearest neighbor algorithm was introduced and used the very basic pattern recognition concepts. 

1979: Students of Stanford University invented the Stanford Cart that navigated the obstacles in the room. 

1997: IBM’s DeepBlue beated the World Champion at Chess. 

2006: “Geoffrey Hinton” coined “Deep learning” and explained new algorithms to let the computer see and distinguish between objects. 

2010: IBM Watson beat its human competitors at Jeopardy. Google Brain was developed, and its deep learning model could work similarly to a cat’s brain. 

2011: The tech giant X Lab developed an ML algorithm that could identify cats in YouTube videos. 

2014: Facebook developed a “DeepFace,” a software algorithm that could recognize and verify individuals in photos similar to humans. 

2015: MNC Companies like Amazon and Microsoft developed their Machine Learning Platforms. 

2016: Google’s AI algorithm beat a professional player in a Chinese board game called “Go.” The AlphaGo algorithm developed by Google DeepMind won 5/5 games in the Go competition. 

2017: Waymo tested autonomous cars in the US, with backup drivers sitting at the back. Later that year, they introduced completely autonomous taxis in Phoenix.

2020: In the midst of the pandemic, OpenAI recently announced its latest development, ChatGPT-3, which is an advanced language model using 175 billion parameters. It was trained using Microsoft Azure’s AI supercomputer and is considered the largest language model to date.

The Future Of Machine Learning.

Boost In Unsupervised Learning Algorithms: Today, there is a lot of space to improve unsupervised machine learning algorithms, and in the future, we can expect a number of innovations to make predictions based on unlabeled datasets. 

The Rise Of Quantum Computing: Quantum computers, being one of the major applications of machine learning, trigger quick data processing and enhance the algorithm’s data analysis proficiency and data-driven decision-making. As we look forward to a rise in machine learning development, trends of quantum computers will also increase. 

Focus On Cognitive Services: With the integration of ML algorithms and AI techniques like natural language processing (NLP), computer vision (CV), and deep learning (DL) into software and applications, features like computer’s cognitive ability, speech, voice recognition and other types of interactions have made technology more accessible to all. In the future, we will see more intelligent applications using cognitive services to boost the market and transform life on Earth. 

Hence, machine learning has become an increasingly important field in recent years, with applications ranging from self-driving cars to personalized marketing. 

To conclude, you now have a mind map of machine learning and a better understanding of the complex relationships between different subfields, algorithms, and applications. As machine learning continues to evolve and improve, it has the potential to revolutionize many industries and aspects of our daily lives. With the rapid pace of technological advancement, it’s clear that AI-driven technology will play an even more significant role in shaping the future. It’s an exciting time to be alive, and humans can’t wait to see what the future holds!

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