Teaching machines to be productive on their own
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Teaching machines to be productive on their own

What is machine learning, and how does it work? Machine learning is a type of artificial intelligence (AI) and computer science that focuses on using data and algorithms to imitate human learning for machines. Machine learning automates analytical model building. It's based on the idea that machines can learn from data, spot patterns, and make decisions without requiring human involvement.

What is machine learning?

Machine learning is a form of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. Machine learning focuses on teaching computers to access and apply data to learn independently.

The “machine learning” term was coined by Arthur Samuel as a game technology in 1959 in his paper "Artificial Intelligence for Games"

The resurgence of interest in machine learning owes, in large part, to the same factors that have propelled data mining and Bayesian analysis to new heights of popularity. Data grows exponentially, and the processing power for crunching it has dramatically improved. These factors mean it's feasible to create models that can analyze bigger, more complex data and provide faster, more accurate results – even on a massive scale – quickly and automatically. And by creating exact models, an organization has a better chance of finding lucrative possibilities or avoiding unforeseen dangers.

How does machine learning work?

Machine learning is based on input to develop knowledge and understanding like the human brain. Machine learning requires data or knowledge graphs to identify things, subjects, and relationships. Deep learning can now begin with entities having been defined.

The machine learning method begins with data, such as examples, firsthand knowledge, or training. It searches for data connections to make inferences based on the provided examples. The fundamental goal of ML is for computers to learn independently without the need for human intervention or assistance and alter their actions as needed.

Machine learning relies on algorithms that encode learning from good data examples into models. Models include categorizing data ("Is this image contains a cat?"), predicting a value for some given data in predefined models ("How likely is this action to be fraudulent?"), and identifying groups in a dataset ("What other products can be recommended to those groups?")

How is machine learning used in various industries?

Machine learning is becoming increasingly prevalent in every industry that works with big data. Organizations can work more efficiently or gain an edge over rivals by extracting insights from this data - frequently in real-time - and many sectors have recognized the value of machine learning technology.


Websites that suggest items you'll like employ machine learning to analyze your purchasing history based on your previous purchases. Retailers use machine learning to gather data, evaluate it, and use it to customize the shopping experience, develop a marketing campaign, price optimize products, plan merchandise releases, and conduct customer research.


Knowing how to extract information from large volumes of data is essential for the transportation sector, which relies on creating more efficient routes and predicting possible difficulties to enhance profitability. Machine learning's data analysis and modeling capabilities are critical tools for delivery organizations, public transit agencies, and other transportation companies.

Financial services

Banks and other financial services companies use machine learning for two purposes: to find important insights in data and to prevent fraud. The insights may help investors identify potential investment opportunities or advise when to trade. Data mining can also determine clients with high-risk profiles or track warning signs of fraud using cyber-surveillance.


Wearable devices and sensors that can use real-time data to assess a patient's health have fueled the development of machine learning in the healthcare sector. Medical professionals can also use the technology to analyze data for trends or warning signals that might lead to better diagnoses and treatment.


The oil and gas industry uses machine learning to find new energy sources, study minerals in the ground for analysis, forecast refinery sensor failure, and improve oil distribution to make it more efficient and cost-effective by streamlining operations. The possibilities for machine learning use in this sector are endless and constantly growing.


Because public safety and utility agencies have a wide range of data sources to draw insights, they constantly need machine learning. Analyzing sensor information, for example, may help you save money and improve efficiency. Machine learning can also be used to spot fraud and reduce identity theft.

Machine learning methods

There are three types of machine learning: Supervised, unsupervised, and reinforcement. Supervised learning involves a teacher or master pattern fed into the system to teach it how to perform a task. Unsupervised learning involves using data collected without human input to develop something new by analyzing patterns in the data. Reinforcement learning engages algorithms and systems to train machines.

Supervised learning

Supervised machine learning is a type of artificial intelligence that involves using labeled data to train algorithms to classify or predict outcomes correctly. The model adjusts its weights until it has been fitted properly as new input data is fed. Cross-validation is used to verify that the model does not overfit or underfit. Supervised learning allows businesses to tackle huge volumes of data at scale, such as spam filtering in a separate folder from your inbox. In supervised learning, bayesian networks, Naïve Bayes, Linear Regression, Logistic Regression, Random Forest, Support Vector Machine (SVM), and other algorithms.

One of the best ways to see the difference between supervised and unsupervised learning is to examine how to learn chess

Unsupervised machine learning

Unsupervised machine learning algorithms analyze and cluster data sets without labels. These methods find concealed patterns or groupings in the information that do not involve users. Its ability to find analogies and distinctions in data gives it the ideal tool for exploratory data analysis, cross-selling tactics, customer segmentation, and image and pattern recognition.

Dimensionality reduction is a technique for removing irrelevant information from a model, which is sometimes necessary when using machine learning to categorize and analyze data. PCA and SVD are two common ways that dimensionality reduction is used. Neural networks, k-means clustering, probabilistic clustering algorithms, and more are examples of supervised learning algorithms.

Semi-supervised learning

Semi-supervised learning is a type of supervised learning in which it combines features from two unlabeled data sets to create a third labeled data set. It uses a smaller labeled data set to assist classification and feature extraction from a larger, unclassified data set during training. The issue of lacking labeled data can be addressed by semi-supervised learning.

Reinforcement learning

The goal of reinforcement machine learning is to produce actions and discover errors or rewards in the environment. The most important features of reinforcement learning are trial and error searching and delayed reward. This technique allows machines and software agents to correctly determine the optimum action in a given scenario to maximize performance automatically. The reinforcement signal, a form of simple reward feedback, is required for the agent to learn which behavior is best.

Positive reinforcement

Positive reinforcement learning gives positive feedback to encourage the machine to increase the probability of repeating the same behavior when an expected behavior pattern is exhibited. This approach encourages repetition of success with rewards given to a child when they pass their class with good grades.

Negative reinforcement

Negative reinforcement aims to increase the chance of a particular positive behavior occurring by removing the negative situation. For example, computer play is restricted and negatively reinforced when a child fails his exams. Rather than directly punishing the child for failing, this move eliminates an adverse condition (in this case, overplaying) that could have caused a student to fail.

What is the difference between supervised vs. unsupervised learning?

One of the best ways to describe the difference between supervised and unsupervised learning is to examine how to learn chess. Yes, you read that correctly: playing chess!

Your first option would be to learn the game from a chess master. A tutorial can teach you how to play the game of chess by explaining the basic rules, what each piece does, and more. Once you know the rules of the game and the abilities of each piece, you can go ahead and practice by playing against your trainer. The trainer will monitor your movements in supervised learning and correct you when you make a mistake. After gathering enough knowledge and practice, you will be ready to play competitively against opponents. As you see, in supervised learning, the data scientist acts like a teacher. It trains the machine by feeding ground rules and general strategy.

Working with a teacher isn't the only way to learn something. You can still learn the chess game if you don't want to hire a pro. You can learn chess by watching how other people play the game. You probably won't be able to ask them questions. But you can watch and learn how to play the game.

Even if you don't know the name of each chess piece, you can learn how each piece moves by observing the game. The more games you watch, the better you will understand and know more about the different strategies you can apply to win.

Both learning methods have remarkable strengths and weaknesses

The data scientist allows the machine to learn by observing in unsupervised learning. Although the machine does not know the specific names or labels, it can find the patterns independently. Unsupervised learning is the technique in which only a training dataset is given to the algorithm.

It helps if a machine had a knowledgeable mentor who could teach the rules and strategy in supervised learning, just like you need a teacher to learn chess. Otherwise, you may learn the game wrong.

In unsupervised learning, you need a huge amount of data for the machine to observe and learn. Although unlabeled data is cheap and easy to collect and store, it should be free of duplicate or garbage data. Defective or incomplete data can cause machine learning bias. That causes the machine cannot produce discriminatory results.

Again, as in chess, if you learn the game by observing other players, you have to watch dozens of games before wrapping your head around it. The trouble is that you can do the same if you're watching people playing the game wrong.

Another technique that we should mention is semi-supervised learning. As you can imagine, semi-supervised learning combines supervised and unsupervised learning. A data scientist trains the machine to get a high-level overview of this learning process. The machine then learns the rules and strategy by observing the patterns. Here a small percentage of the training data will be labeled, and the rest will be unlabeled. If we continue the example of learning chess, semi-supervised learning can be likened to a teacher who explains the basics to you and lets you learn by playing competitively.

How is training data used in machine learning?

The training data is the first dataset used to train machine learning algorithms. Models create and refine their rules using this data.

Training data is the primary input that makes up the machine learning model. It teaches machines what is expected of them. The model analyzes the dataset to gain a deeper understanding and mastery of the requirements.

Training data is classified into two categories: labeled and unlabeled data.

Labeled data

Labeled data is a group of data samples labeled with one or more meaningful labels. Also called annotated data. Tags identify certain properties, classifications, or objects. For example, fruit photos can be labeled more as apples, bananas, or grapes.

Labeled training data is used in supervised learning. It enables ML models to learn the properties of specific tags to classify new data points. Labeled data collection is a challenging and costly process. Compared to unlabeled data, storing labeled data is also a critical process.

Unlabeled data

Unlabeled data do not have any tags to describe classifications or features. It is used in unsupervised machine learning. It requires ML models to find patterns or similarities in the data to arrive at results.

Let us continue with the example of apples, bananas, and grapes. AI experts will not label photos of these fruits in the unlabeled training data. Instead, the model will evaluate each image by looking at its characteristics, such as color and shape.

How much training data does an ML algorithm need?

How much training data will be needed depends on what you expect from the algorithm you train. Suppose you want to train a text classifier that categorizes sentences based on the terms "cat" and "dog" in addition to their synonyms, such as "little cat," "kitten," or "puppy." You won't need any large datasets as there are only a few terms to match and sort.

However, if your project were to develop an image classifier that classifies photos into "cats" and "dogs," the data points you would need in the training dataset would greatly increase.

Many factors come into play to decide which training data is sufficient. The amount of data required will vary depending on the algorithm used. For example, a context-aware deep learning algorithm would require millions of data points to train neural networks. In contrast, most machine learning algorithms only need thousands of data points.

What is deep learning?

Deep Learning (DL) is the ability of intelligent machines to learn and comprehend. It is designed with inspiration from the structure and working principle of the human brain. It is a critical technology that makes autonomous vehicles a reality. Deep learning allows us to get closer to creating machines that think and feel like us.

Deep learning is a sub-branch of machine learning that mimics the functioning of the human brain for data processing. It allows machines to learn without human supervision. It gives the ability to perceive what is spoken, translate it, identify objects, and make decisions.

Despite being a branch of machine learning, DL systems do not have limited learning capabilities like traditional machine learning (ML) algorithms. Instead, DL systems can continually improve their performance as they are fed larger and more consistent data sets.

How does deep learning work?

In deep learning, the training process changes the system actions based on the feedback loop. There is a reward for every right action and a penalty for wrongdoing in the learning system. The system tries to improve its actions to maximize the rewards.

Processing unstructured big data is almost impossible for the human mind. Even if you acquire the necessary workforce, it can take years to uncover valuable information in large datasets. Deep learning has made this process surprisingly simple.

Deep learning allows artificial intelligence systems to train and improve without human supervision. DL also allows machines to learn from unlabeled and unstructured data.

What is an artificial neural network?

Deep learning makes it possible for AI systems to mimic the way humans learn. DL algorithms try to conclude by constantly analyzing data. Artificial neural networks (ANNs) are used to achieve this, defined as an electronic version of the human brain. Artificial neural networks have been developed so that machines can exhibit human characteristics such as problem-solving abilities, self-awareness, perception, creativity, and empathy.

Deep learning would not be possible if computers did not become more affordable, faster, and smaller. The same is true for storage devices, as large amounts of data must be stored and processed for deep learning to become a reality. Therefore, although deep learning was theorized in the 1980s, its real-world implementation had to wait.