The concept of machine learning is vastly comprehensive when you get into its subsets of it. However, there is always a starting point even for the most complex of technologies and that is what we will cover in this Machine Learning tutorial.
In this article, we will explore several intricate aspects of Machine learning including:
Introduction to Machine learning
Seems like you would have stumbled upon the term machine learning and must be wondering what exactly it is. Well, this machine learning tutorial will clear out all of your confusion!
Machine learning is a field of artificial intelligence with the help of which you can perform magic! Yes, you read it right. Let’s take some real-life examples to understand this. I believe all of you must have heard of Google’s self-driving car. A car that drives by itself without any human support; is just amazing, isn’t it?
Now, how about virtual personal assistants such as Apple’s Siri or Microsoft’s Cortana? If you ask Siri what is the distance between Earth and Moon, it will immediately reply that the distance is 384,400km.
You also must have used Google maps. If you want to go from New Jersey to New York via road, google maps will show you the distance between these two places, the shortest route, and also how much traffic is there along the road.
Now, you would agree with me that all of these are magical applications, and the magic behind these applications is machine learning. So, simply put, machine learning is a sub-domain of artificial intelligence, where a machine is provided data to learn and make insightful decisions.
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Why do we need Machine Learning?
Data today has become the backbone of any business. Companies are increasingly relying on Data-driven decisions as they create a distinction between keeping up with the competition or falling further behind. Machine learning or ML can be the key to unlocking the true value of corporate and customer data and deriving insights and decisions that keep a company ahead of the competition.
How Does Machine Learning Work?
Machine learning is made up of three parts:
- The computational algorithm is at the core of making determinations.
- Variables and features that make up the decision.
- Base knowledge for which the answer is known enables or trains the system to learn.
Initially, the model is fed parameter data for which the answer is known. The algorithm is then run, and adjustments are made until the algorithm’s output (learning) agrees with the known answer. At this point, increasing amounts of data are fed to the system for it to learn and process higher computational decisions.
History of Machine Learning
The term machine learning was first coined in 1959 by Arthur Samuel, an IBM employee, and pioneer in the field of computer gaming and artificial intelligence.
By the early 1960s an experimental “learning machine” with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyze sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. This machine was frequently trained by a human operator to recognize patterns and was equipped with a ‘goof’ button to reevaluate incorrect decisions.
In 1981, a report was given on using teaching strategies so that a neural network learns to recognize 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.
Modern-day machine learning has two objectives, one is to classify data based on models which have been developed, and the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning to train it to classify cancerous moles.
Features of Machine Learning
Automation: Your Gmail has a lot of emails in the spam folder. Most of these emails are either unwanted forwards or sometimes even potentially harmful online links. Now you must be wondering how Gmail knows which emails need to be segregated in the said folder. The answer is Machine learning. With its ML algorithm, Gmail recognizes spam emails, and thus, it is easy to automate this process. The ability to automate repetitive tasks is one of the biggest characteristics of machine learning. A huge number of organizations are already using machine learning-powered paperwork and email automation. In the financial sector, for example, a huge number of repetitive, data-heavy, and predictable tasks are needed to be performed. Because of this, this sector uses different types of machine learning solutions to a great extent.
Improved customer experience: One of the primary ways used by businesses to drive engagement, promote brand loyalty, and establish long-lasting customer relationships is by providing a customized experience and providing better services. Machine Learning helps us to achieve both of them. Have you ever noticed that whenever you open any shopping site or see any ads on the internet, they are mostly about something that you recently searched for? This is because machine learning has enabled us to make amazing recommendation systems that are accurate. They help us customize the user experience. Now coming to the service, most of companies nowadays have a chatting bot with them that are available 24×7. An example of this is Eva from AirAsia airlines. These bots provide intelligent answers and sometimes you might even not notice that you are having a conversation with a bot. These bots use Machine Learning, which helps them to provide a good user experience.
Automated data visualization: In the past, we have seen a huge amount of data being generated by companies and individuals. Take an example of companies like Google, Twitter, and Facebook. How much data are they generating per day? We can use this data and visualize the notable relationships, thus giving businesses the ability to make better decisions that can benefit both companies as well as customers. With the help of user-friendly automated data visualization platforms such as AutoViz, businesses can obtain a wealth of new insights to increase productivity in their processes.
Business intelligence: Machine learning characteristics, when merged with big data analytics, can help companies to find solutions to the problems that can help the businesses to grow and generate more profit. From retail to financial services to healthcare, and many more, ML has already become one of the most effective technologies to boost business operations.
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Types of Machine Learning Algorithms
Machine learning has been broadly categorized into three categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
What is Supervised Learning?
Let us start with an easy example, say you are teaching a kid to differentiate dogs from cats. How would you do it?
You may show him/her a dog and say “here is a dog” and when you encounter a cat you would point it out as a cat. When you show the kid enough dogs and cats, he may learn to differentiate between them. If he is trained well, he may be able to recognize different breeds of dogs that he hasn’t even seen.
Similarly, in Supervised Learning, we have two sets of variables. One is called the target variable, or labels (the variable we want to predict) and features(variables that help us to predict target variables). We show the program(model) the features and the label associated with these features and then the program can find the underlying pattern in the data.
Thus, we can say that the supervised learning model has a set of input variables (x), and an output variable (y). An algorithm identifies the mapping function between the input and output variables. The relationship is y = f(x).
The learning is monitored or supervised in the sense that we already know the output and the algorithm are corrected each time to optimize its results. The algorithm is trained over the data set and amended until it achieves an acceptable level of performance.
We can group the supervised learning problems as:
Regression problems – Used to predict future values and the model is trained with the historical data. E.g., Predicting the future price of a house.
Classification problems – Various labels train the algorithm to identify items within a specific category. E.g., Dog or cat( as mentioned in the above example), Apple or an orange, Beer or wine or water.
What is Unsupervised Learning?
This approach is the one where we have no target variables, and we have only the input variable(features) at hand. The algorithm learns by itself and discovers an impressive structure in the data.
The goal is to decipher the underlying distribution in the data to gain more knowledge about the data.
We can group the unsupervised learning problems as:
Clustering: This means bundling the input variables with the same characteristics together. E.g., grouping users based on search history
Association: Here, we discover the rules that govern meaningful associations among the data set. E.g., People who watch ‘X’ will also watch ‘Y’.
What is Reinforcement Learning?
In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period.
Reinforcement learning is different from supervised learning in the sense that there is no answer available, so the reinforcement agent decides the steps to perform a task. The machine learns from its own experiences when there is no training data set present.
In this Machine Learning tutorial, we are going to mainly focus on Supervised Learning and Unsupervised learning as these are quite easy to understand and implement.
Types of Machine Learning Algorithms
One of the most time-consuming and difficult processes in your journey of Machine Learning is learning about the diverse range of algorithms. There are many algorithms in Machine Learning and you don’t need to know them all to get started. But I would suggest, once you start practicing Machine Learning, start learning about the most popular algorithms out there such as:
SVM
Every algorithm has its magic. The demand for data forced every data scientist to learn different algorithms. Most industries are deeply involved in Machine Learning and are interested in exploring different algorithms. Support Vector Machine is one such algorithm. It is considered the black box technique as there are unknown parameters that are not so easy to interpret and assume how it works. It depends on three working principles:
- Maximum margin classifiers
- Support vector classifiers
- Support vector machines
Decision Tree Classifier
A decision tree is a popular machine learning classifier. So, a decision tree as the name states has an inverted tree-like structure. The topmost node in the tree is known as the root node and the nodes at the bottom of the tree are known as the leaf nodes. Every node has a test condition and based on that test condition, the tree splits into either its left child or right child.
Let’s go through this example on a decision tree. Here, we are trying to determine whether a person would watch the movie “Avengers” based on a series of test conditions.
Here, the test condition on the root node is “likes action films”. If the result is true, you go to the left child, else to the right child. If you like action films, then on the left child, there is another test condition, “Movie length greater than 2 hours”. So, if this evaluates to true, you go again to the left child, i.e., you are fine watching a movie which is greater than 2 hours. Again, when you go to the left child, there is another test condition, “Likes Robert Downey Jr”. Again, if this is true, it means you are looking forward to watching “Avengers”. So, this is how a decision tree classifier works.
KNN Algorithm
KNN belongs to a group of lazy learners. As opposed to eager learners such as logistic regression, SVM, and neural nets, lazy learners just store the training data in memory. During the training phase, KNN arranges the data (sort of indexing process) to find the closest neighbors efficiently during the inference phase. Otherwise, it would have to compare each new case during inference with the whole dataset making it quite inefficient.
So if you are wondering what is a training phase, eager learners and lazy learners, for now just remember that the training phase is when an algorithm learns from the data provided to it. For example, if you have gone through the Linear Regression algorithm linked above, during the training phase the algorithm tries to find the best fit line which is a process that includes a lot of computations and hence takes a lot of time and this type of algorithm is called eager learners. On the other hand, lazy learners are just like KNNs who do not involve many computations and hence train faster.
K-means Clustering
K-means clustering is a non-hierarchical approach to forming good clusters. For K-Means modeling, the number of clusters needs to be determined before the model is prepared. These K values are measured by certain evaluation techniques once the model is run. K-means clustering is widely used in large dataset applications.
As this algorithm is based on distance calculation from each observation to the centroids present and this is an iterative process, the data needs to be in a proper format. In case the dataset has variables with different units of measure, one should undertake the process of Scaling to bring all the variables into one unit/ measure, for further algorithm processing. There are 2 methods of Scaling: Z Scaling and Min-Max Scaling. In Z Scaling, features will be rescaled and it has the properties of a standard normal distribution. In Min Max Scaling, the data is scaled to a fixed range – 0 to 1. Although the cost of having this bounded range of smaller standard deviations can suppress the effect of outliners.
Random Forest Algorithm
As the name suggests, a random forest is nothing but a collection of multiple decision tree models. Random forest is a supervised Machine Learning algorithm. This algorithm creates a set of decision trees from a few randomly selected subsets of the training set and picks predictions from each tree. Then using voting, the random forest algorithm selects the best solution.
Random Forest Example
Let us understand the concept of random forest with the help of a pictorial example.
Say, we have four samples as shown below:
A random forest algorithm will create four decision trees taking inputs from subsets, for example,
The random forest algorithm works well because it aggregates many decision trees, which reduces the effect of noisy results, whereas the prediction results of a single decision tree may be prone to noise.
Random forest algorithms can be applied to build both classification and regression models.
- In the case of a random forest classification model, each decision tree votes; then to get the final result, the most popular prediction class is chosen.
- In the case of a random forest regression model, the mean of all decision tree results is considered the final result.
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Advantages and Disadvantages of Machine Learning
- Easily identifies trends and patterns
Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. For instance, e-commerce websites like Amazon and Flipkart serve to understand the browsing behaviors and purchase histories of their users to help cater to the right products, deals, and reminders relevant to them. It uses the results to reveal relevant advertisements to them.
We are continuously generating new data and when we provide this data to the Machine Learning model it helps it to upgrade with time and increase its performance and accuracy. We can say it is like gaining experience as they keep improving in accuracy and efficiency. This lets them make better decisions.
- Handling multidimensional and multi-variety data
Machine Learning algorithms are good at handling data that are multidimensional and multi-variety, and they can do this in dynamic or uncertain environments.
You could be an e-tailer or a healthcare provider and make Machine Learning work for you. Where it does apply, it holds the capability to help deliver a much more personal experience to customers while also targeting the right customers.
Disadvantages of Machine Learning
Machine Learning requires a massive amount of data sets to train on, and these should be inclusive/unbiased, and of good quality. There can also be times when we must wait for new data to be generated.
Machine Learning needs enough time to let the algorithms learn and develop enough to fulfill their purpose with a considerable amount of accuracy and relevancy. It also needs massive resources to function. This can mean additional requirements of computer power for you.
- Interpretation of Results
Another major challenge is the ability to accurately interpret results generated by the algorithms. You must also carefully choose the algorithms for your purpose. Sometimes, based on some analysis you might select an algorithm but this model doesn’t need to be the best for the problem.
- High error-susceptibility
Machine Learning is autonomous but highly susceptible to errors. Suppose you train an algorithm with data sets small enough to not be inclusive. You end up with biased predictions coming from a biased training set. This leads to irrelevant advertisements being displayed to customers. In the case of Machine Learning, such blunders can set off a chain of errors that can go undetected for long periods. And when they do get noticed, it takes quite some time to recognize the source of the issue, and even longer to correct it.
Go through these Top 40 Machine Learning Interview Questions and Answers to crack your interviews.
Applications of Machine Engineering
Machine learning is a buzzword for today’s technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google Assistant, Alexa, etc. Below are some most trending real-world applications of Machine Learning:
Image Recognition
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion:
Facebook provides us with a feature of auto friend tagging suggestions. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning’s face detection and recognition algorithm.
It is based on the Facebook project named “Deep Face,” which is responsible for face recognition and person identification in the picture.
Speech Recognition
While using Google, we get an option of “Search by voice,” which comes under speech recognition, and it’s a popular application of machine learning.
Speech recognition is a process of converting voice instructions into text, and it is also known as “Speech to text”, or “Computer speech recognition.” At present, machine learning algorithms are widely used in various applications of speech recognition. Google Assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow voice instructions.
Traffic prediction
If we want to visit a new place, we take the help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions.
It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:
- Real-Time location of the vehicle from Google Map app and sensors
- Average time has taken on past days at the same time.
Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends it back to its database to improve its performance.
Product recommendations
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendations to the user. Whenever we search for some product on Amazon, then we start getting an advertisement for the same product while internet surfing on the same browser, and this is because of machine learning.
Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest.
Similarly, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.
Self-driving cars
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company, is working on self-driving cars. It is using an unsupervised learning method to train the car models to detect people and objects while driving.
Email Spam and Malware Filtering
Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail:
- Content Filter
- Header filter
- General blacklists filter
- Rules-based filters
- Permission filters
Future of Machine Learning
Machine Learning can be a competitive advantage to any company, be it a top MNC or a startup. As things that are currently being done manually will be done tomorrow by machines. With the introduction of projects such as self-driving cars, and Sophia(a humanoid robot developed by Hong Kong-based company Hanson Robotics) we have already started a glimpse of what the future can be. The Machine Learning revolution will stay with us for long and so will the future of Machine Learning.
Now, if you are interested in doing an end-to-end certification course in Machine Learning, you can check out Intellipaat’s Machine Learning Course with Python.