What is Machine Learning? Everything you Need to Know

“When Machine Learning, Natural Language Processing, and Neural networks combine- Artificial Intelligence is made”

From driverless cars to translating speeches in different dialects, there is hardly anything machine learning algorithms are not eyeing upon. Using AI in its mechanism, ML is a boon for the software development industry making sense of all the unpredictable real-world happenings.

That chatbot offering to-the-point information is Machine Learning, and the personalized shopping experience Zara is providing is also Machine Learning. Outlining Machine Learning introduction, we are knowingly or unknowingly already using ML to a great extent. 

Here in this machine learning guide, we will be taking a tour of all about machine learning and understanding its A-Z. So, let’s get started- 

What is Machine Learning?

Introduction to machine learning is a mechanism of teaching a computer system to make accurate predictions when filled with data.

These computers are hence capable of identifying, analyzing, and changing the information as per the requirements accordingly eliminating the need for human interactions. 

The machine learning algorithms, their mechanism, and the power behind machine learning are self-identification and analyzing new patterns involved in complex pattern recognition algorithms. 

Timeline of Machine Learning

Machine Learning is not new, it was an invention made way back. Starting from Voice-activated home appliances, self-driving cars, and online marketing to healthcare, Machine learning is benefiting us every other day.

Starting from mathematicians to great thinkers and scholars were involved in the foundation of how to start machine learning.

Here is how machine learning has evolved- 

Great Evolution of Machine Learning

1812-1913- Foundation of Machine Learning

It is the mathematical foundation that laid the foundation for machine learning. The Bayes theorem & Markov’s Chains gave birth to innovation.

The late 1940s- Introduction of the First computers

Computers are still known as a machine that can store trillions of data. The very popular Manchester Small-Scale Experimental Machine (widely known as Manchester Baby’) is the presentation of this era.

1950- The birth of Machine Learning

Before 1950, there was a lot of research and theoretical studies made for Machine learning, but the year 1950- is marked as the real birth of machine learning.

Alan Turing, the famous mathematician, researcher, computer genius, and thinker, submitted a paper called “imitation game” and made the world astonished. 

1951- First Neural Network

The very first neural network was made by Marvin Minsky with Dean Edmonds in 1951. Neural networks connect the thinking process of machines and computers. 

1974- Naming of Machine Learning

It was 1974, the year coined the name for machine learning from the proposed words like Informatics, computational intelligence, and artificial intelligence.

1996- Game Changer

IBM’s Deep Blue computer beat the world-famous champion, Garry Kasparov, in chess. This proved that machines can also think like humans.

2006-2017- the year of Backpropagation, External Memory Access, and AlphaGo

Back Propagation is the technique that is used by the machine for image recognition. 

Also, Neural Network was developed by DeepMind– A British-based company. The network can access external memory and get things done. 

AlphaGo, in 2016 was designed by DeepMind Researchers. It beat world-famous Go players Lee Sedol and Ke Jie and also proved that machines can also think like humans. 

The future

ML with the support of AI is entering our personal lives helping through voice-activated devices, smart systems, automated systems, and more. This is called singularity which is done to make an impact in the life of individuals.

Types of Machine Learning

Types of machine learning are divided into different sections- 

Learning Algorithms

• Supervised Learning
• Unsupervised Learning
• Semi-Supervised Learning

Similarity Algorithms

A. Regression Algorithm

• Linear Regression
• Logistic Regression

B. Decision Tree Algorithm

C. Artificial Neural Network

D. Support Vector Machine

Reinforcement Learning

Let’s discuss them in detail- 

Learning Algorithms

One of the most important and popular algorithms of AI is Learning Algorithms. When we say what is machine learning used for, these algorithms are easily adaptable to the problem environment. Also, these algorithms are divided into certain sections known as- 

• Supervised Learning Algorithms
• Unsupervised Learning Algorithms
• Semi-supervised Learning Algorithms

Supervised Learning Algorithms

Supervised Learning is learning about a function that is appropriate for a particular problem. 

The function takes input from (X) and maps it as output to (Y). The equation somewhere looks like Y= f(X). Furthermore, the Dataset is split into 2 groups- The training Dataset and the Testing/Validation Dataset.

Supervised learning problems and then grouped them into classification problems and regression problems. In classification problems- Output is categorized into a specific group and Regression Problems are all about an output with real value.

Unsupervised Learning

Unsupervised Learning caters to the input dataset which is not labeled, Classified, or categorized. The mathematical model integrated into it helps in identifying similarities in the dataset while deducing a structure presented in the input data. 

Furthermore, Unsupervised Learning Problems are grouped into clustering problems and association problems. 

Semi-supervised Learning Algorithms

Semi-supervised Learning and input dataset is a combination of both Labeled data and unlabeled data. This dataset carries a small amount of labeled data along with a large amount of data. 

Moving on, the mathematical model makes use of labeled data to learn the structure of unlabeled data for predictions. 

Similarity Algorithms

Similarity Algorithms, one of the machine learning algorithms are classified as per the similarity of their functionality.

A. Regression Algorithm

Because of the relationship between variables, Statistical Machine Learning co-opted regression methods. Regression algorithms can refine the relationships between them to predict a better outcome.

The algorithm is further divided into- 

I.  Linear Regression – This Regression is in the use for the last 200 years for removing variables from correlates. Also, regression is used for removing noisy data from the dataset. 

II. Logistic Regression– Logistic regression is majorly used for binary classification problems. The reason for using it is to get the weights of input variables separately whereas the output is used for nonlinear functions. 

B. Decision Tree Algorithm

Just like beginners guide to natural language processing is all about processing the language, similarly, the machine learning algorithm is meant for constructing a model of a decision fully based on the input variables. A decision tree mimics a binary tree, where each decision node is a single input variable. Also, these given nodes are then split into leaf-like nodes. And each leaf node is decked with output variables concerning the previously made nodes.

C. Artificial Neural Network

Also known as the Connectist system, ANN works like the brain’s neural network. Artificial Neural Networks is a framework made from various machine learning algorithms working together for processing the complete dataset. 

The ANN is broadly divided into 3 layers- the input layer, hidden layers, and the output layers. Hidden layers are further categorized into multiple layers. Each layer of it is a collection of nodes known as artificial neurons. 

Furthermore, these artificial neurons connect to the artificial neural present in the coming layer. The connection made between neurons is called edges. 

The edges are important for transmitting information as a signal from one neuron to the next one. 

D. Support Vector Machine

The next point in this machine learning guide is Support Vector Machine (SVM). The SVM mechanism is related to the supervised learning approach and is meant for classifying and regression. 

Reinforcement Learning

Reinforcement Learning is the next required part of the machine learning tutorial. It is a subfield of machine learning known as approximate dynamic programming or neurodynamic programming. 

Markov Decision Process (MDP)is the mechanism of the Reinforcement Learning ecosystem. 

To understand machine learning algorithms, let us take an example. 

Suppose, a child is sitting in a room and sees a fireplace in the room and tries to approach that fire. He feels the warmth of it and finds it positive. This makes him realize that fire is a positive thing. But when a child touches it, the fire burns the child’s hand. This made him realize that fire is a good thing to a certain extent. This is how reinforcement learning understands and learns from the actions made. 

With the types covered, let us now check out how Machine Learning works in the next section of our machine learning guide.

How Machine Learning Works

The above-mentioned points about the types of Machine Learning here in this ultimate machine learning guide are now enough to explain how ML works.

ML uses two techniques for processing the work- 

• Supervised Learning- It is meant for known input and output data for predicting the future outcome, 
• unsupervised learning- is all about finding the hidden patterns or structures in the input data. 

Attending to this, the next machine learning tutorial for beginners is about the Machine learning algorithm

Machine Learning Algorithms

Other than –  Regression, Decision trees, and Artificial Neural Network Algorithms, Machine Learning Algorithms are more than that. 

Let’s get started with them one by one here in the next section of the ultimate machine learning guide.

Instance-based algorithms

This machine-learning algorithm is a model that works for decision problems using examples or instances of training data. 

The methods here build up a database of example data and then compare the new data to the database. All this happens using a similarity measure for figuring out the prediction. 

The most popular instance-based algorithms are:

• Support Vector Machines (SVM)
• Locally Weighted Learning (LWL)
• Learning Vector Quantization (LVQ)
• Self-Organizing Map (SOM)
• k-Nearest Neighbor (CNN)

Regularization Algorithm

It is an extension made to regression methods that penalized models as per the complexity level. 

The most popular regularization algorithms are:

• Least-Angle Regression (LARS)
• Elastic Net
• Ridge Regression
• Least Absolute Shrinkage and Selection Operator (LASSO)

Bayesian Algorithms

The next machine learning algorithms example out of many is the Bayesian Algorithm. Bayesian applies Bayes’ theorem to solving problems like regression, classification, etc. 

The most popular Bayesian algorithms are:

• Bayesian Network (BN)
• Averaged One-Dependence Estimators (AODE)
• Bayesian Belief Network (BBN)
• Naive Bayes
• Gaussian Naive Bayes
• Multinomial Naive Bayes

Clustering Algorithms

One of the next machine learning algorithms examples is clustering. Clustering describes a class of problems and the methods required for the class. 

The methods involved in this are organized by the modeling approaches like hierarchical and centroid. 

The most popular clustering algorithms are:

• Expectation Maximisation (EM)
  • k-Means
  • k-Medians
• Hierarchical Clustering

Association Rule Learning Algorithms

The association in this machine-learning algorithm extracts rules that can showcase the relationship between variables in data.

These said rules are capable of finding out the most important and commercially useful associations made in multidimensional datasets. 

The most popular association rule learning algorithms are:

• Apriori algorithm
• Eclat algorithm

Dimensionality Reduction Algorithms

Dimensionality reduction algorithms seek and exploit the structure in the data. This machine-learning algorithm is useful when it comes to visualizing dimensional data or simplifying data. 

The following methods are used for this kind of algorithm mechanism

• Linear Discriminant Analysis (LDA)
• Mixture Discriminant Analysis (MDA)
• Partial Least Squares Regression (PLSR)
• Sammon Mapping
• Quadratic Discriminant Analysis (QDA)
• Flexible Discriminant Analysis (FDA)
• Multidimensional Scaling (MDS)
• Projection Pursuit
• Principal Component Analysis (PCA)
• Principal Component Regression (PCR)

Ensemble Algorithms

These are the methods that are made from multiple weaker models independently trained. Also, the predictions are intelligently being made for further analysis.

Meanwhile, here are a few popular techniques used-

• Gradient Boosting Machines (GBM)
• Weighted Average (Blending)
• Stacked Generalization (Stacking)
• Gradient Boosted Regression Trees (GBRT)
• Random Forest
• Boosting
• Bootstrapped Aggregation (Bagging)
• AdaBoost

The next section of this ultimate machine learning guide will highlight examples of it. 

4 Examples of Machine Learning

Speech Recognition

The best examples of speech recognition are voice-based searching and call rerouting all the while using ML. Here the principle majorly focuses on translating spoken words into text and then using them based on the frequencies. 

Image Recognition

This is one of the practices we do when using Google Drive or Photos. Here the principle is all about classifying the pictures as per the intensity (black and white) and measurement of intensities related to RGB colors.

Financial Sectors

Just like AI in Fintech, ML being the subset is also transforming the financial domains smartly. Machine Learning here helps in predicting external financial threats, and frauds, along with seeing customers’ credit habits, transaction patterns, etc. 

Healthcare

Artificial intelligence is one of the top tech trends in healthcare and is in use along with Machine Learning. Diagnosing various diseases is increasingly being made using ML. 

When these 4 points are about the examples of ML being used, the next section of the machine learning guide is an elaborative version of what ML is used for. 

What is Machine Learning used for

Machine learning is all around us. We are knowingly or unknowingly also using it whereas the data gathered from our activities are being used for enhanced user experience. Hence, what is machine learning used for in various proven cases? 

Here in this machine learning guide, we will be showing you how ML is used through examples. 

ML is used for guiding one to recommend what product they might want to buy next or which services they want to watch next on Amazon, or Netflix. 

When it comes to tech giant Google, it uses Ml for understanding user queries in a particular language for personalized results. Next, Gmail’s feature that detects spam and phishing also uses machine learning-trained models. This makes the inbox free from spam or rogue messages.

Other top examples shown by various giants are Apple’s Siri, Amazon’s Alexa, Microsoft Cortana, and Google Assistant. Google Assistant Development along with others is quite in use for detecting cases and things related to novel coronavirus.

When this was about the languages and personalized experience, ML is being used for driverless cars, drones, delivery robots, chatbots, and many more. Not just the role of chatbots in enterprises, these AI-powered chatbots provide COVID-19 information too.

Also, ML is used for facial recognition and diagnosis purposes.

These practices take us to the next section of how ML is used in various industries.

Which Industries use Machine Learning

We have seen how ML is used by various industries whereas technology has the potential to deal with industries that deal with volumes of data, and complex systems.

Let’s get started with what machine learning do to various industries.

Pharmaceutical

Machine learning while cutting the cost of drug designs and testing helps in obtaining results with accuracy through the data. The data involves the entire data about the drugs and the chemical compounds used in them. For accurate results, various other parameters are also taken into consideration.

Banks and Financial Institutions

When we say how to use machine learning, Banks and financial institutions prove how to use it. The industry uses ML for attracting investors’ attention along with using methods for increasing investments. Also, other than that, ML is also used for detecting financial frauds, user buying and spending patterns, transactions, and cybersecurity threats. 

Healthcare

Just like IoT in healthcare, ML is used for predicting possible symptoms of diseases based on medical history, genetics, and lifestyle. Not just that, ML is also capable of alerting one about the possible health threats they might encounter. 

Also, wearable designing technology- an amalgamation of AL and ML are quite in demand for helping people globally.

Online Sales

Top online shopping companies by using ML tracks the habits of online shoppers like what kind of products they use, and what generally they search for. Other than that, related ads, and offer discounts, are also being placed using ML. All of these things are made to provide a personalized experience to the user.

Mining, Oil, and Gas

Machine Learning other than predicting diseases, and online shopping habits, helps in predicting the best location for the availability of minerals, oil, gas, and other natural resources. This happens all the while eliminating the need to invest huge amounts and manual power. 

Government Schemes

Governmental organizations are also using ML to check what the people demand and how to fulfill their needs. 

Science

Just like AI, ML being the subside helps in studying stars, planets, and other celestial bodies without spending huge amounts and labor. 

This was all about Machine learning. And this is not the end, there are many more things, ML is capable of doing. 

Moving on, the next section of this machine learning guide will be about how businesses are using this technology. 

How do Businesses use Machine Learning?

One should learn about how to learn machine learning from businesses or from anyone who is experimenting with this technology for a better outcome. 

With the introduction of machine learning and Artificial Intelligence, Business applications have emerged from a few issues- 

• Big data was too bulky and disorganized for training computers to work accordingly.
• There was no robust computing power that could make ML practically workable. 

Thankfully, ML is in the market now, helping businesses through various business applications. Let’s have a look at them- 

eCommerce Applications

As we have mentioned earlier that eCommerce giants are using ML and ML-based applications to streamline their processes for enhanced user experience. Meanwhile, this is how they are using it. 

CLV modeling- With the help of ML,  it can check total customer value along with learning the early indicators.                

Pricing- While analyzing the behavioral indicators, the external factors are also taken into account for dynamic pricing in real-time. 

Recommendations- Using ML, recommendations about a product or service can be made to the users in real time. This will not just help the user but also enhance their experience to the fullest. 

Smart Hiring

For big organizations, hiring is a challenging process. Also, when it comes to hiring people for a high-end profile, choosing the best candidates becomes a task. Hence, organizations are using the power of ML to screen resumes and identify top performers for hiring purposes. 

When this was just the process, here is the list of companies using ML- 

Companies using Machine Learning

Google DeepMind platform

Google’s DeepMind platform is focused on using machine learning for all research purposes along with researching on the tools, and on the other applications. 

DeepMind isn’t a platform other organizations can take leverage from.

Microsoft Azure AI platform

This platform uses machine learning to analyze images while making predictions using data. 

Azure is built for offering edge computing functionality for insights and data-driven decisions. The mechanism is fairly simple deployments for developers to run experiments. 

IBM Watson Platform

This is one of the major Watson’s AI Platforms, it’s an “open, multi-cloud platform letting automate the AI lifecycle.

Amazon AI Platform

Like IBM, Amazon AI Platform offers ML that’s quick and easy to use-

• AI services help in solving common ML problems (recommendations, forecasting, image analysis)

Understanding the core of ML, let us now check out why ML is successful or will remain useful for the coming years. 

What is the future of Machine Learning

As per Gartner- 

“Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing, or application”

Machine learning is slowly and gradually taking baby steps in the world. Changes along with a great impact on the life of the people around the corner can be expected. 

And it will not be surprising if Machine Learning soon will- 

• Enter every aspect of human life.
• Entering cloud-based services.
• Being omnipresent in business & industries irrespective of the firm size.
• Changing the CPU designing too for computational efficiency. 

Conclusion

What is machine learning used for, it is a little dicey to understand what it will bring the world. Machine learning is different in terms of dependency while impacting everyday life carrying the power to work wonders. At the same time, machine learning is impacting the world in a better way in Healthcare, the finance industry, image processing, voice recognition, the automotive industry, and many other fields.

If you get more information and an in-depth understanding of Machine Learning, connect with our experts too.