Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.
It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications, and its importance. To help you understand this topic I will give answers to some relevant questions about machine learning.
But before we answer these questions, it is important to first know about the history of machine learning.
A brief history of machine learning
You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.
In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.
In 1959 Bernard Widrow and Marcian Hoff created two neural network models called Adeline, which could detect binary patterns and Madeline, that could eliminate echo on phone lines.
In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.
Gerald DeJonge, in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.
During the 1990s work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.
Now that we know about the origin and history of ml, let us start by answering a simple question – What is Machine Learning?
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What is Machine Learning?
Have you ever wondered how Facebook’s ‘People you may know’ feature always provide you with a genuine list of people that you actually know in real life and with whom you should connect with on Facebook as well? How does Facebook come to know about this? How are they doing this recommendation?
Well, Machine Learning is an answer to this question.
Machine learning definition according to Tom Mitchell:
“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience”
In simpler words, machine learning is the field of computer science which makes the machine capable of learning on its own without being explicitly programmed.
The point to be noted here is that ML algorithms can learn on its own from past experiences, just like humans do. When exposed to new data, these algorithms learn, change and grow by themselves without you needing to change the code every single time.
So basically, what happens is that, instead of you writing the code every single time for a new problem, you simply feed the data to the ml algorithm and the algorithm/machine builds the logic and provides results based on the given data.
Initially, the results obtained might not be of high accuracy but, over time the accuracy of ml algorithms become higher as it continuously performs tasks.
How do Machine Learning algorithms work?
Machine Learning algorithms utilize a variety of techniques to handle large amounts of complex data to make decisions.
These algorithms complete the task of learning from data with specific inputs given to the machine. It’s important to understand how these algorithms and a machine learning system as a whole work, so that we can get to know how these can be used in the future.
It all starts with training the machine learning algorithm by using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction.The predictions and results are evaluated for accuracy.
If the prediction is not as expected, the algorithm is re-trained again and again until the desired output is obtained. This enables the ml algorithm to learn on its own and produce an optimal answer that will gradually increase in accuracy over time.
After a desired level of accuracy is obtained, the machine learning algorithm is deployed.Let me explain to you how machine learning works with a simple example:
When you search for “Lion images” on Google Search (as seen in the image below), Google is incredibly good at bringing relevant results, but how does Google achieve this task?
- Google first gets a large quantity of examples(datasets) of photos labeled “LION”.
- Then the Machine learning algorithm looks for patterns of pixels and patterns of colors that will help it predict if the image is of “LION”.
- At first, Google’s computers make a random guess of what patterns are good in order to identify an image of a LION.
- If it makes a mistake, then a set of adjustments are made in order for the algorithm to get it right.
- In the end, such collection of patterns will be learned by a large computer system modeled after the human brain, that once trained can correctly identify and bring accurate results of LION images on Google Search.
If you were in charge of building a machine-learning algorithm to try and identify images between lions and tigers. How will you go about it?
The first step as I explained above would be to gather a large number of labeled images with “LION” for lions and “TIGER” for tigers. After this, we will train the computer to look for patterns on the images in order to identify lions and tigers respectively.
Once the machine learning model has been trained, we can give it(input) different images to see if it can correctly identify lions and tigers separately. As seen in the image above, a trained machine learning model can correctly identify such queries.
Now that we know how machine learning algorithm works, we should dive a bit deeper into this topic and explore various types of machine learning.
Also Read: 10 Innovative Machine Learning Project Ideas
Types of Machine Learning
Machine Learning is broadly divided into three main areas, supervised learning, unsupervised and reinforcement learning. Each one of these has a specific action and purpose, yielding particular results by using various types of data.
1. Supervised machine learning
Supervised learning in simple language means training the machine learning model just like a coach trains a batsman. In Supervised Learning, the machine learns under the guidance of labeled data i.e. known data.
This known data is fed to the machine learning model and is used to train it. Once the model is trained with a known set of data, you can go ahead and feed unknown data to the model to get a new response.
2. Unsupervised machine learning
Unsupervised machine learning in simple language means the ml model is self-sufficient in learning on its own.
In unsupervised machine learning, there is no such provision of labeled data. The training data is unknown or unlabeled. This unknown data is fed to the machine learning model and is used to train the model.
The model tries to find patterns and relationships in the dataset by creating clusters in it. The thing to be noted here is that unsupervised learning is not able to add labels to the clusters.
For example, it cannot say this is a group of oranges or mangoes, but it will separate all the oranges from mangoes.
3. Reinforcement machine learning
In this, the machine learns from a hit and trial method. Whenever the model predicts or produces a result, it is penalized if the prediction is wrong or rewarded if the prediction is correct. Based on these actions the model trains itself.
Want to know which skills are required to become a machine learning engineer? Check out the video below to know more.
After understanding the basic concepts and types of Machine learning, I think now we are in the right position to understand its importance and its applications.
Why is Machine learning Important?
“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” —– Andrew Ng
I think most of you will agree with this. It is quite hard to think of any industrial activity which can be done without the use of Machine learning or Artificial Intelligence.Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems efficiently, effectively and quickly.
To better understand the importance of machine learning let me go ahead and list certain instances where Machine learning is applied: online recommendation engines from Facebook, Netflix, Amazon, Apple’s Siri responding to your queries, facial recognition.
It is quite hard for you to think of performing the above-mentioned tasks without the use of machine learning.
Applications of Machine learning
Machine learning is everywhere. Because of a wide range of applications of machine learning, it is possible that you might be using it in one way or the other and you don’t even know about it.Below I will be listing a few applications of machine learning.
1. Virtual Personal Assistant: Siri, Alexa, Google some of the common examples of virtual personal assistants. These assist in finding information when asked over voice. While answering your query, these personal assistants’ lookout for information recalls your related queries or sends a command to other resources in order to collect information.
Machine learning is an integral part of the functioning of personal assistants as they collect and refine the information on the basis of your previous queries. Later this refined dataset is used to give results that are tailored to your preferences.
2. Facial recognition: You simply look at your phone and the phone unlocks. The camera in your phone recognizes unique features and projections on your face using image processing (part of machine learning) in order to identify that the person unlocking the phone is not someone else but you.
The entire process at the back end is complicated but seems to be a simple application of ML at the frontend.
3. Email spam filter: How does your mailbox automatically identify if the email you received is spam or not? Well, again here ML is to thank for. The email spam filter uses a supervised machine learning model to filter out spammy emails from your mailbox.
4. Recommendation engine on an e-commerce website: Have you ever wondered how Amazon or Flipkart shows relevant products after you make a purchase from their platform. This is the magic of ML.
Once a user buys something from an e-commerce website it stores the purchase data for future reference and finds products that are most likely to be bought by the user in the future. This is possible because of the machine learning future algorithm model, which can identify patterns in a given dataset.
Also Read: How Machine Learning is Used in Finance
Some other applications of machine learning include:
- Online fraud detection
- Social Media Services such as “People you may know” on Facebook, “Similar pins” in Pinterest
- Online Customer Support i.e. Chatbot
- Search Engine Result Refining
- Predictions while commuting using Google Maps
Prerequisites for machine learning
If you are interested in learning more about machine learning, a few requirements should be met to be in order to excel in this field. These requirements include:
- Basic knowledge of programming languages such as Python or R.
- Good knowledge of statistics and probability
- Understanding of linear algebra and calculus
- Data modelling to find variations and patterns in a given dataset
All the above-mentioned prerequisites are a must in order to learn machine learning.
If you want to learn machine learning from scratch you can check out Verzeo’s Machine Learning pro degree certification course. You need not worry about the prerequisites mentioned above as all it will be covered in the pro degree certification.
I hope I have cleared all your doubts related to machine learning and its application.