This Tutorial Explains What is Machine Learning, How Does It Work, Applications of ML and The Comparison of Machine Learning Vs Artificial Intelligence:
Machine Learning is a field in computer science that learns from experience without being programmed. It is a part of Artificial Intelligence, or we can say that machine learning is a sub-topic of Artificial Intelligence.
The science behind ML is to make computers perform actions by themselves. A Machine Learning algorithm is a generic program that will understand the data, and build models with that data. These models are available for the end-users to carry out tasks.
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Table of Contents:
- Complete List of Machine Learning Tutorials
- Overview of Tutorials in This Machine Learning Series
- Introduction to Machine Learning (ML)
- What is Machine Learning?
- Evolution of ML
- How Does Machine Learning Work?
- Machine Learning and Artificial Intelligence
- Comparison of Machine Learning and Artificial Intelligence
- Deep Learning
- Applications of Machine Learning
- #1) Product Recommendations on E-Commerce Sites
- #2) Face Recognition During Photo Tagging
- #3) Recognition of Speech
- #4) Route and Traffic Suggestions
- #5) Price Recommendations During Online Booking
- #6) Social Media Marketing
- #7) Email Spam Detection
- #8) Medical Diagnosis
- #9) Online Customer Service
- #10) Search Engine Suggestions
- Conclusion
Complete List of Machine Learning Tutorials
Tutorial #1: Introduction To Machine Learning & Its Applications (This Tutorial)
Tutorial #2: Types Of Machine Learning: Supervised Vs Unsupervised Learning
Tutorial #3: A Complete Guide To Artificial Neural Network In Machine Learning
Tutorial #4: Neural Network Learning Rules: Perceptron & Hebbian Learning
Tutorial #5: Artificial Neural Network Models: Multilayer Perceptron & Others
Tutorial #6: Introduction To Genetic Algorithms In Machine Learning
Tutorial #7: What Is Support Vector Machine (SVM) In Machine Learning
Tutorial #8: Weka Tutorial–How To Download, Install And Use Weka Tool
Tutorial #9: WEKA Dataset, Classifier And J48 Algorithm For Decision Tree
Tutorial #10: WEKA Explorer: Visualization, Clustering, Association Rule Mining
Tutorial #11: Deep Learning Vs Machine Learning: Key Differences
Tutorial #12: Decision Trees in Machine Learning
Overview of Tutorials in This Machine Learning Series
| Tutorial # | What You Will Learn |
|---|---|
| Tutorial_#1: | Introduction To Machine Learning & Its Applications This Simple Introductory Machine Learning Tutorial will focus on concepts like What is Machine Learning, How Does It Work, Applications of ML along with the Comparison of Machine Learning Vs Artificial Intelligence. This tutorial will indeed act as a base when you proceed with the other tutorials in this Machine Learning series. |
| Tutorial_#2: | Types Of Machine Learning: Supervised Vs Unsupervised Learning A Detailed explanation of the various Types of Machine Learning i.e. Supervised, Unsupervised, Reinforcement & Semi Supervised Learning is included in this tutorial with simple examples. |
| Tutorial_#3: | A Complete Guide To Artificial Neural Network In Machine Learning This Machine Learning tutorial aims at explaining you What an Artificial Neural Network is? How Does An ANN Work? The Structure and Types of ANN, along with the Neural Network Architecture in simple terms. |
| Tutorial_#4: | Neural Network Learning Rules: Perceptron & Hebbian Learning This in-depth tutorial on Neural Network Learning Rules will explain you all about Hebbian Learning and Perceptron Learning Algorithms with perfect examples. |
| Tutorial_#5: | Artificial Neural Network Models: Multilayer Perceptron & Others This tutorial explains Artificial Neural Network Models – Multilayer Perceptron, Backpropagation, Radial Bias & Kohonen Maps including their Architecture in detail. |
| Tutorial_#6: | Introduction To Genetic Algorithms In Machine Learning Genetic Algorithms are algorithms based on the evolutionary idea of natural selection & genetics. This tutorial explains all about Genetic Algorithms in ML. |
| Tutorial_#7: | What Is Support Vector Machine (SVM) In Machine Learning This tutorial explains Support Vector Machine. SVMs are mathematical supervised ML algorithms extensively used in the classification of training data set. |
| Tutorial_#8: | Weka Tutorial–How To Download, Install And Use Weka Tool This WEKA tutorial explains what is Weka Machine Learning tool, its features, and how to download, install, and use Weka Machine Learning Software. |
| Tutorial_#9: | WEKA Dataset, Classifier And J48 Algorithm For Decision Tree This tutorial explains WEKA Dataset, Classifier, and J48 Algorithm for Decision Tree. Also provides information about sample ARFF datasets for Weka. |
| Tutorial_#10: | WEKA Explorer: Visualization, Clustering, Association Rule Mining This tutorial explains how to perform Data Visualization, K-means Cluster Analysis, and Association Rule Mining using WEKA Explorer. |
| Tutorial_#11: | Deep Learning Vs Machine Learning: Key Differences Comprehensive guide to understand the difference between Deep Learning and Machine Learning, and gain practical hands-on experience |
| Tutorial_#12: | Decision Trees in Machine Learning An extensive study of Decision Trees in Machine Learning with Advantages, Disadvantages and various terminologies implemented. |
Introduction to Machine Learning (ML)
Over the past few years, Machine Learning has become the center of focus in the field of information technology and is a part of human life as well. As data is increasing day by day, strong and smart data analysis has become a need for all technological processes.
Machine Learning is a key to the problems where we don’t want to invent the code for every new application. With machine learning, we somewhat form prototypes to reduce the range of different kinds of problems. Some of the well-known applications that we see around include speech recognition, self-driving cars, web search recommendations, etc.
Thus, the central idea of machine learning is to build computer programs that perform certain jobs (tasks) which when fed with data, can learn automatically from that data by themselves (experience) and improve their performance (performance).
This performance is improved with experience. It is an iterative process. The different kinds of problems that go through these ML algorithms result in solutions that are reliable and good to be taken as the prototype and rely on results.
What is Machine Learning?
Machine Learning is a discipline for artificial intelligence for building computer programs that automatically improve through experience and make predictions.
Let’s see a practical application for understanding the need for ML. There are various internet stores such as Amazon, Flipkart, eBay, etc., which use the past purchasing history and past viewing of the user to attract users to buy some additional items.
Using this information these sites will predict the users’ future purchasing and viewing of products. The idea behind this is that these sites will analyze purchases, wish lists, carts, and the views of similar users. It is always desired to make this whole process automatic to avoid any efforts in performing guesses and thereby save a lot of time.
The above example clearly tells us how machine learning plays a vital role in today’s world. Machine learning helps in taking out useful information from huge volumes of data that help the organizations to make major business-related decisions.
Machine Learning is employed for tasks that are very cumbersome and complex for a human to work on. These tasks are fed to machine learning algorithms for exploration and build models for achieving the desired goals.
Evolution of ML
The term Machine learning was given by Arthur Samuel in the year 1959 in the computer gaming and artificial intelligence field.
Later in the year 1997, Tom Mitchell gave it a standard definition as “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with the experience E.”
Initially, Machine Learning was just about pattern recognition. It was also defined as the ability of the computers to learn through an iterative process without being programmed explicitly.
With increasing data day by day, and invent of big data, machine learning has taken a fresh turn. Now machine learning algorithms are able to automatically calculate highly complex calculations over big data.
These mathematical calculations are being done at a high speed and accuracy. Some major examples in this field includes fraud detections, online recommendations, etc.
How Does Machine Learning Work?
Machine learning is a set of algorithms that perform a certain task with the input data and also improve their performance. These algorithms match the input to the output, thereby resulting in the prediction of patterns. The more data is fed to the algorithms, the more accurate the predictions are.
Let us have an overview of how machine learning actually works:
- A machine learning algorithm is fed with a training dataset to build a prototype or a sample.
- Now, a data model is already built-in step 1. Whenever a new test data is fed into the algorithm, it will make predictions according to the built model.
- The resulting prediction may or may not be accurate. This accuracy is checked by the error rate. If the accuracy falls below the prescribed error level, then the algorithm is fed again with the training data.
- Else, if the resulting prediction falls above the level i.e. it can be accepted, then this algorithm is put in the machines for use.
The steps listed above are the general steps that are followed by all machine learning algorithms. The algorithms in simple terms are just the methods that perform certain tasks.
How are the algorithms able to predict the outcome for a particular input? The answer to this comes in the form of a target function (f).
Target Function (f)
It is a target function that drives all of the machine learning algorithms. This target function maps the input to the output. It gives the best results.
For an input “a”, the output “o” can be predicted as:
o= f(a)
Learning Function
The learning function learns from the training data set so that it matches the target function in the predicting outcome i.e. for any value of input “a”, it is able to predict the value of “o”. However, it is an iterative process.
With each iteration, an error margin or performance level (b) is checked. This error is added to the predicted output.
Hence the output can be predicted as:
o = f(a)+b
This learning function is very important to make correct predictions otherwise the algorithm will be of no use.
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Machine Learning and Artificial Intelligence
Machine Learning and Artificial Intelligence are the two important terms in computer science that are often used interchangeably by users. While it is not so, machine learning is just a subfield of Artificial Intelligence.
First, let us understand Artificial Intelligence in detail.
Artificial Intelligence in literal terms means making artificial things intelligent. Unlike humans, who have inborn intelligence, the ability to think and make decisions, machines are just dumb systems with no brains.
Hence making a machine to work as a human, think like humans and have decision-making capability is termed as artificial intelligence. Artificial Intelligence is a study of training the machines and the other devices to perform jobs as humans do.
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Comparison of Machine Learning and Artificial Intelligence
| Machine Learning | Artificial Intelligence |
|---|---|
| Function | |
| Machine learns from data and predicts the output. | The AI systems perform tasks as smart machines. |
| Working | |
| Machine Learning is used to make the machine accurate for a certain task. | AI systems work to solve complex problems like humans but automatically and without human interference. |
| Focus Area | |
| ML algorithms focus on accuracy and reduction of error of the algorithm. | AI systems focus on success of the system rather than how accurate it is. |
| Process | |
| ML algorithms are continuous learning processes to make machines more accurate. | Artificial Intelligent machines are decision making systems. |
| How it works? | |
| ML increases the knowledge of the machine by feeding it with data and optimizing the solution. | AI systems becomes smart and intelligent through extensive programming. |
| All machine learning devices are Artificially Intelligent systems. | All AI systems are not regarded as machine learnt. |
| Algorithms | |
| Machine Learning are knowledge based and does not require human intervention. They are self-learning. | AI algorithms are series of if-then statements complied by humans. These systems work on rules. |
| A Machine Learning algorithm has the ability to make modifications to itself when fed with input data, without human intervention. Thus are less reliant on humans. | Artificially Intelligent systems cannot modify themselves, they need experts to modify the code. |
| Applications | |
| Some applications of ML include face recognition, self-driving cars by Google. | Some applications of AI systems are expert systems, knowledge graphs, symbolic AI. |
Deep Learning
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Deep learning is a subtopic of machine learning. Deep Learning uses the Artificial Neural Networks (ANN) concept of Machine Learning to solve problems and induce human-like decision-making process, etc. Deep Learning process can be categorized into supervised, unsupervised and semi-supervised deep learning.
The ANN model is similar to that of a human brain. The ANN is structured and they work like the biological neurons of the human brain. The neurons in the human brain correspond to nodes in an ANN.
ANN has various layers from input to output. These layers are called hidden layers and a network can have one or more hidden layers. Deep Learning has many applications such as language translation, handwriting recognition & recognition of characteristics, image search, etc.
Applications of Machine Learning
Machine Learning has taken roots in our everyday lives. Some way or the other, we are using AI to run our lives. ML is a subset of Artificial Intelligence. We don’t realize, how deeply ML has become a part of us, hence let’s have a look at some of the real applications of ML.
#1) Product Recommendations on E-Commerce Sites
Whenever we shop at a certain site, we often see that the next time we log in, it will show some similar product suggestions and combos to buy. The site would send us emails regarding the matching products.
If we use a Mobile App to shop, then the app notifications will be sent showing the discount codes, similar products, often bought together recommendations, people also viewed suggestions, color options of the searched items and so many things like this.
All the above recommendations are to make the shopping experience of the customer better and easier. It is machine learning which does all this. It goes through the customer profile, wish list, orders, items in cart, and analyzes it to make predictions of the items.
#2) Face Recognition During Photo Tagging
Certain web applications such as Facebook, suggests the user with the name of the friends which are in the photograph. The user then tags his friend with that suggestion. In our mobile phones, the photos often show tagging options with the names of people who are in the photo on our contact list.
This feature is enable by the Machine Learning Facial Recognition algorithm. This algorithm runs in the web applications and all other photo tagging applications.
#3) Recognition of Speech
Devices like Google Alexa, Amazon Echo are able to provide us with the information based on what we ask them using speech. If we ask them to set an alarm or search for a word meaning or sing a song or flight timings etc., it recognizes our words, searches on the internet and accordingly gives us suggestions through speech.
This feature is enabled by ML Speech Recognition Algorithms. These algorithms collect information, process the information, refine it based on the user’s past communications with the devices.
#4) Route and Traffic Suggestions
Applications like Google maps suggest the best route to follow to reach our destination. These suggestions are given on the basis of calculations made from the past data of speed, locations of vehicles, etc. It will store all the information in a central server.
Machine Learning algorithms help us in congestion and best route analysis.
#5) Price Recommendations During Online Booking
Cab booking apps such as Uber, OLA use Machine Learning for price recommendations at different hours of the day. The price surges and price dips are based on data collected from previous bookings and fed to machine learning algorithms. These apps then provide prices for cab booking according to the rider’s demand.
#6) Social Media Marketing
Applications such as Facebook marketplace, Facebook ad campaigns, Google AdWords, FB news feed, Sponsored Ads in our news feed, People you may know, etc. use Machine Learning for making these suggestions.
#7) Email Spam Detection
Some mails in our mailbox are directly moved to the spam box. This process happens due to ML technique which tracks the spam tricks used by spammers. These algorithms are regularly updated to become more effective.
If we apply rules for spam detection then the algorithm will fail to track the spams at times. ML methods such as Perceptron, Decision Tree Induction, etc., are used for this.
#8) Medical Diagnosis
Using Machine Learning, the medical specialists are able to track the progression of the disease, find out parameters and combination of these parameters which led to the progression of the disease.
It also helps in treatment planning and patient management. With ML predictions, medical experts can enhance the work environment and improve the efficiency of care.
#9) Online Customer Service
Nowadays, many websites use an automated chatbot to answer the questions of the users present on their web page. The chatbots ask questions for understanding the user query and then give solutions based on the answers provided by users.
This information is extracted from the website and shown to the user thus enhances the user experience as well as reduces the workload over customer service representatives. This is only possible through Machine Learning Algorithms.
#10) Search Engine Suggestions
Google, Bing, Yahoo Search Engine, etc., show the results based on the words written in the search box. So whenever the user searches something and checks the result, the machine learning algorithms keep tracking the user activity to refine the results next time.
It will note how many times you opened the webpage shown in the results, on which page of results (1st, 2nd or 3rd page, etc.) were you able to find the appropriate web page that you were looking for, etc. Using this, the search engine can provide better suggestions next time.
Conclusion
Machine Learning is a part of Artificial Intelligence which can make predictions using pattern and trends recognition in data. The ML algorithms have self-learning capabilities and do not require human interference for error calculation.
ML algorithms adapt themselves on their own and learn from the previous data to show results for the new data fed into the system and also identify the hidden trends and patterns in the data. It is an iterative process.
Artificial Intelligence is a field of computer science that makes artificial things such as computers, or other devices intelligent by feeding them with data and code. These devices are then able to behave like humans.
There are various machine learning algorithms and tools that are available for businesses to use. The only key here is that the business should know which is the right algorithm and the right tool to build a machine learning model for their organization’s benefit.
Today there is a need for robust algorithms as data is growing with lightning speed every day. With Big Data, it is impossible for humans to manually extract information from raw data. Hence, there is a pressing need for some automated process to process useful information from the unstructured raw data.
Machine Learning has many real-life applications that we see around us but fail to realize. Some major applications of Machine learning are online cab service price recommendations, product recommendations on shopping sites, facial recognition, speech recognition, etc.
Check out our upcoming tutorial to know more about the various types of Machine Learning!!