What is Machine Learning?

Machine learning is a fragment of artificial intelligence (AI) that commonly necessitates advancing algorithms and statistical models that assist the computer in functioning and acquiring the data. It also helps the computer to make prognoses and conclusions from the given data, without being explicitly programmed to perform a certain task. It is more like instructing computers to learn and analyze data and make decisions on their own. Machine learning has its eminence as it is used in several applications, including speech and image recognition, fraud detection, Google, Uber, and many more. Data Science and Artificial Intelligence (AI) skills are in big demand. Whether you’re a new graduate, an experienced professional, or simply looking to advance your career, now is the right time to develop skills in these thrilling fields.

How Does Machine Learning Work?

Based on preceding experiences AI trains the computer to contemplate and process like a human mind. The maxim of machine learning is to replace the human mind with machines. Machines are anticipated to make settlements by interpreting, recognizing patterns, and analyzing data. Human participation is expected to be little to zero. Any task or business that has interpreted data patterns or set of rules can be automated by using Machine learning (ML). Machine learning has broadly helped businesses to fully automate their business activities. Formerly customer support executives were required to handle customer queries and problems. Now we have bots to operate customers. This way Machine learning has taken over the market. For example, in Korea, there is a department store that is completely and wholly automated. It has no human interlinkage. We have machines there to handle clients and to do the billing. Amazing, no? This is the finest example of Machine learning (ML).

Machine Learning Methods

Machine learning (ML) uses different approaches consisting of supervised, semi-supervised, unsupervised, and reinforcement learning.

Supervised Learning

Supervised machine learning is a type of machine learning that operates under supervision as suggested by the name. In this type of machine learning, we train the machines by giving a well-tagged and labeled set of data. By learning from the data and making methodical use of the given “labeled training data” machines predict the output. Please note that the “labeled data” inclusive of input data and response values given to the computer acts as a supervisor that helps the machine to derive attainable and accessible output. In uncomplicated words, it acts like a student learning and rehearsing under the direction of his teacher. This method involves giving the machine learning model the correct input data as well as the output data. We have a data set and we predict the outcome of new data depending on the behavior of the existing data sets. That means the existing data set acts as a guide or supervisor to find the new data. A very common example is housing price prediction, email filtering, and credit scoring.

Main Types of Supervised Learning

Regression: Regression is a type of supervised learning technique that is used to predict continuous values. It draws a curve between the set of data. For example, predicting house prices, or estimating patient recovery time.

Classification: Classification is a type of machine learning technique that involves organizing data into distinct categories based on specific criteria. It makes data management much easier by organizing the data resulting in informed and quick decision-making.

There are many different types of supervised machine learning algorithms, some of the majorly used algorithms are:

• Linear regression
• Decision tree
• Random forests
• Logistic regression
• Support vector machines
• Naive Bayes

Unsupervised Learning

Unsupervised learning is a machine learning technique where you do need to supervise the model. Instead of that, you need to let the model operate on its own and make decisions. In this technique, the model discovers information and it mainly and significantly deals with unlabeled data. First, we input the raw data into the system( mostly unlabeled data with no training data set), and the model interprets the data resulting in an understanding of the algorithm. After this, it processes the data and gives out an attainable output. Some examples of unsupervised learning are matrix factorization, dictionary learning, and recommendation engines.

Main Types of Unsupervised Data

Clustering: Clustering is the process of collecting unlabeled data in one place. It mainly involves clustering the data points in one place that differs from others. In simple words, it is segregating groups having similar data sets into clusters.

Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people who buy X also tend to buy Y.

There are many different types of unsupervised machine learning algorithms, some of the majorly used algorithms are:

• Anomaly detection
• Principal component analysis
• K-means clustering
• LDA

Semi-supervised Learning

It is a blend of supervised and unsupervised learning. In semi-supervised learning, an algorithm is trained on a dataset that has some labeled data and some unlabeled data. So, it’s like the computer is getting some help, but it still has to figure out some things on its own. An example of semi-supervised learning is text document classifiers.

How Does Semi-supervised Learning Work?

In this technique, a computer uses the data to get a slight idea of what exactly is it looking for. It then uses this information to make educated guesses about the unlabeled data. Essentially, it uses the labeled data as a starting point to learn about the relationships between the different variables and make predictions about the rest of the data. The goal is to extract patterns or relations from the set of annotated data and generalize them on the unsupervised data.

Reinforcement Learning

Reinforcement Learning (RL) is a sub-field of Machine Learning where the aim is to create agents that learn how to operate optimally in a partially random environment by directly interacting with it and observing the consequences of its actions (a.k.a. the rewards and punishments it gets). It’s based on the observation that intelligent agents (such as humans) tend to repeat the actions that were rewarded for and refrain from actions that were punished for.

Deep Learning vs Machine Learning

• Deep learning can analyze and learn data and make intelligent decisions on its own whereas Machine learning (ML) uses instructions and algorithms to pursue data, learn from the data, and make decisions based on what it has learned.
• Deep learning is a sub-field of machine learning that powers most human-like artificial intelligence (AI) whereas Machine learning is a subset of AI.
• Machine learning is more explicitly used around the globe as it has simple methods, while deep learning makes use of advanced methods.
• Machine learning needs more human involvement whereas, Deep learning interprets the data on its own from the environment and past mistakes.
• Machine learning can be trained on a Central processing unit (CPU) whereas, Deep learning needs a specialized (GPU) graphics processing unit
• Machine learning has a shorter training period whereas, Deep learning has a longer training period.

AI vs Machine Learning

AI refers to the overall idea of creating intelligent machines, while Machine Learning is a specific approach within AI that focuses on enabling machines to learn from data and improve their performance on tasks. AI can include other techniques beyond Machine Learning, such as rule-based systems and expert systems, making it a broader concept.

Real-World Machine Learning Uses

• Fraud detection: Machine learning helps in identifying fraudulent links, and behavior in financial transactions. This technique can especially be used by banks and other financial institutions to spot unusual and fishy behavior.
• Image and face recognition: Algorithms in Machine learning can be trained in a way that they recognize patterns in images and audio. It allows ML applications to identify faces, objects, and spoken words. The most widely used example of this is your mobile phone’s face recognition system which allows you to unlock your phone with face ID, or fingerprints.
• Recommendation systems: Machine learning algorithms can analyze user behavior and preferences to recommend products or services that are likely to be of interest to them. This technology is used in applications like personalized advertising, movie or music recommendations, and product recommendations on e-commerce websites.
• Natural language processing: Machine learning algorithms can analyze text and understand natural language, allowing them to perform tasks like language translation, sentiment analysis, and speech-to-text transcription.
• Education: Machine learning could be used to personalize education for different students based on their requirements.
• Transportation: Self-driving cars heavily rely on Machine learning to interpret data and make real-time decisions.
• Climate change: Machine learning algorithms could be used to analyze large amounts of data to predict and mitigate the impact of climate change.

Challenges of Machine Learning

• Minimal data: Real-world situations mostly do not have huge amounts of data. So, the algorithm must learn from small data but still generalize.
• Reducing computing requirements: Unless the algorithm is truly efficient, it will never be usable in real situations since people expect hardware usage concerning the value created.
• Explainability: However accurate and consistent the result an algorithm produces, people will still have questions on why it has generated certain output. So, directly through an algorithm or indirectly by processing additional data, we should be able to explain the results, when asked.
• AI impacts on jobs: This concern should be highlighted as advancement in technology is greatly impacting job opportunities. Businesses are automating their business activities which were earlier performed by humans. Every addition or advancement in technology creates a slack in the number of jobs available.

Conclusion

Machine learning is an enabling technology – it allows us to develop software solutions much faster than before. It’s a lot like how programmable computers were an enabling technology back then, that allowed us to design computer systems much faster (than having to create a new circuit for every new problem).

In the beginning, we had to design a new circuit for each new problem. Then we just had to design a new program for each new problem. Now, we just have to design a machine-learning architecture for each new category of problems (image, audio, control, etc).

Take a look at your smartphone. It’s a supercomputer in your pocket. You don’t need to type, now you swipe. You don’t want to swipe? “ok Google!” and now you talk to it. It is a SMART phone. Anyone around pre-1994? Do you recall what the internet was good for pre-1994? That’s not the Jurassic period – that was merely two decades ago.

So it makes sense that 20 years from now, what computers will be able to do will blow your mind. Just as it blew our minds when Google recently announced that DeepMind had beaten a 2 dan at Go. Computers are learning. Whoever programs Machine Learning is in effect giving birth to our future.