Have you been asking the question What is machine learning? Do you want to know how to learn machine learning?
Talk about applications that learn from experience and improve their decision-making or predictive accuracy over time. Yes, devices are getting smarter. Smart entrepreneurs make smart choices. They integrate Machine Learning and Artificial Intelligence to strengthen their niche from complex robots to smart autoresponders.
What is machine learning? Why does it matter, and what is this fuss about it? Can machine learning make your revenue even better?
YES! Get the edge ahead of your competition. This post will give you everything you need to know as well as how to learn machine learning and AI. It will also answer the question What is machine learning?
In the next few lines, we’ll talk about details that will help you answer the question “What is machine learning?”
What is Machine Learning?
Here we will shed more light on the question What is machine learning?
Machine learning is a subset of Artificial Intelligence that allows a system to learn and make decisions from experience without programming.
There are algorithms to make the computer smart enough to make choices based on data without any human intervention.
An algorithm is a set of rules or processes that a computer must follow in problem-solving operations. These algorithms are designed to identify patterns in big data and use these patterns to make decisions and predictions.
There are several examples of machine learning around us :
- If you bought, searched, or even talked about something with your phone, websites will recommend products, movies, and songs based on that item.
- Some robots will vacuum your floors without your help.
- Your email provider helps you drop unwanted emails into spam.
- Doctors can spot tumors from medical image analysis systems.
- And yeah, you must've heard of self-driving cars.
More innovations are coming. Big data is getting bigger. There are more powerful and affordable computers now, and more data scientists are handling and developing more capable algorithms. Machine learning will continue to bring greater efficiency to our everyday lives.
Machine Learning (ML) or Artificial Intelligence (AI)?
We’ll take it a step further and walk you through the answer to the question “What is machine learning?”
Many people confuse Artificial intelligence and Machine Learning. They are different. Artificial intelligence is a machine's ability to perform tasks commonly done by humans.
So, AI allows machines to execute tasks by "smartly" imitating humans. Machine learning is another branch of Artificial intelligence. It is how the machine learns from the data fed into it as algorithms.
The core of an AI-based system is its model. A model is a program that improves its knowledge through a learning process (algorithms). When data is given as an example to the computer to make decisions on new data, the type of model is categorized as supervised learning.
The opposite of that is the unsupervised learning Model. We will learn about these categories better.
One popular application of Machine Learning that is being recently used globally is image recognition. Applications that recognize images are first "trained." Data scientists will run many pictures through the system and tell the computer what is in each photo.
After several thousands of repetitions, the application learns the patterns of pixels. It identifies the patterns to attribute as horse, dog, cat, flower, tree, etc. pictures.
Many internet-based companies use Machine Learning to power their suggestion engines. For instance, ML enables Facebook to decide what to show in your newsfeed, Netflix to suggest movies you like to watch, and Amazon to highlight products you might want to purchase. They are all based on predictions from patterns in the system's existing data.
I hope so far you are getting insight into the question “What is machine learning?”
Let's talk about how to learn machine learning.
So, how does machine learning work?
How machine learning works
Everything that teaches you how to learn machine learning starts with training a machine learning model, telling the computer a function in its language.
The 'training' requires the specialist to develop a math function that can repeatedly modify its own operations until it can accurately predict outcomes from a new data set.
Machine learning uses processes similar to that of data mining. We say that algorithms are written in computer language. In other words, here is a math expression that can further help in understanding.
Let's describe the algorithm in terms of a target function (f) and say it maps an input variable (x) to an output variable (y).
This would be represented as:
Now, there will be a margin for error. Let's call that e. It is the independent part of the input variable x.
y=f(x) + e
To build a machine learning application (or model), a data scientist must follow four basic steps. The data scientists would typically work closely with the business owners that the machine would serve. This is the first step to learning how to learn machine learning works.
Step 1: Select and prepare a training data set
What is the training data? It is a data set that represents the data that the machine learning model will be fed to solve the problem it's designed to solve.
Data can either be labeled or unlabelled. The training data must be adequately prepared, de-duped, randomized, and checked for imbalances or biases that could affect the training. The data must come as two subsets:
- The training subset to train the application and
- The evaluation subset to test and refine the machine.
Step 2: Choose the algorithm to run on the training data set
As we said earlier, an algorithm is a process or set of rules that a computer must follow in problem-solving operations. The type of algorithm the data scientist chooses depends on the problem to be solved.
These are the most common types of machine learning algorithms used with labeled data:
- Regression algorithms
- Decision trees
- Instance-based algorithms
These are some standard algorithms used with unlabeled data:
- Clustering algorithms
- Association algorithms
- Neural networks
Step 3: Train the algorithm to create the model
Next, you get to the real deal. Training the algorithm. This is an iterative process. You have to run variables through the algorithm and compare the output with the results it should have produced.
You would adjust the weights and biases within the algorithm that might yield a more accurate result and rerun the variables until the algorithm produces the correct result most of the time. The resulting precise algorithm is the machine learning model.
Step 4: Using and improving the model
This is the last part. You will run the model through new data to improve its accuracy and efficiency over time.
Machine Learning Methods
There are basically three machine learning methods, or as many experts call it, machine learning styles. These will guide you on what you need to know about how to learn machine learning.
Supervised machine learning
In this method, the computer is fed with the input and output data and feedback during the training. The main goal of supervised training is to make the system learn how to map the input to the output.
Hence, the machine trains itself based on the example humans sets for it. For example, an ML model designed to identify pet and security animals might be trained on a data set of various labeled animal pictures.
Unsupervised machine learning
The unsupervised machine learning method is when you provide no such training, but you leave the computer to find the output on its own. This method is mostly used on transactional data. It is applied to more complex tasks.
Unsupervised machine learning uses a strategy called deep learning to arrive at some conclusions. We will explain what deep learning means later. But really, when using this method, the computer uses set algorithms to extract meaningful features it needs for labeling, sorting, or classifying data without human intervention.
Unsupervised machine learning is about identifying patterns and relationships in data that humans would miss. It is the method used in spam detection systems.
One setback of any Supervised Learning algorithm is that the ML engineer or data scientist has to hand-label the set data. It is a very costly process, especially when dealing with large volumes of data. For any Unsupervised Learning, it can be frustrating that the application spectrum is limited.
As a counter to these setbacks, Semi-Supervised Learning was introduced. In this method, the algorithm is trained through a combination of labeled and unlabeled data. The programmer will first cluster similar data using an unsupervised learning algorithm. He then uses the existing labeled data to label the rest of the unlabeled data.
Semi-Supervised learning is like teaching the computer a few concepts and then giving it questions as homework based on similar concepts.
The semi-supervised machine learning method is the in-between of supervised and unsupervised machine learning. During the training, the system uses a smaller data set to guide classification and feature extraction from a broader data set.
Reinforcement machine learning
Reinforcement machine learning uses three components—agent, environment, action. The agent perceives the computer's surroundings, The agent interacts and acts with the environment. It is similar to supervised learning, but in this case, the model learns as it goes by trial and error.
Deep learning is used in machine learning. This is one of the important points when it comes to how to learn machine learning. Algorithms define an artificial neural network designed to learn new experiences, just like the human brain.
Deep Learning algorithms apply many layers of processing. Every layer uses the output of the previous layer as an input for the next.
The neural networks that work deep learning models are slow at their inception, and they might take a while to show results like the mind of a newborn baby.
As the data scientist exposes the system to the needed data, the system fine-tunes its accuracy for highly sophisticated and advanced tasks.
Deep learning is all about neural networks. In theory, an Artificial Neural Network (or ANN) are interconnected artificial neurons made to exchange data as they network. The neurons get updated as it gets new data knowledge and experience.
CNN (Convolutional Neural Network)
This kind of neural network involves applying multiple independent filters over a multi-channeled image to extract some contrasting and distinct features from an image. It is used in DIP applications.
RNN (Recurrent Neural Network)
Simply, RNN is used to process patterns of data and information where the previous sets of outputs or results can be used to predict the results of the next set of outputs on a set of new data. The most common use of RNN is the automatic suggestions you get on platforms such as Netflix, Amazon, Spotify, etc.
Real-world Uses of Machine Learning
As we said earlier, machine learning is everywhere and once you discover how to learn machine learning then you will see its application in everyday life.
Let's look at a few examples of machine learning you see every day:
- Digital assistants : Amazon Alexa, Google Assistant, Apple Siri, and other digital assistants you may know are powered by natural language processing (NLP). This machine learning application enables computers to process text and voice data and 'understand' human language the way people do.
- Recommendations : Deep learning models power the 'people also liked' and the 'just for you' suggestions you encounter on Facebook, Netflix, Spotify, Amazon, and other online services.
- Online advertising : Advertisers use ML and deep learning models to analyze a web page's content to identify the author's opinion or attitude and bring up advertisements that suit the visitor's interests.
- Chatbots : Chatbots and autoresponders are usually a combination of pattern recognition, natural language processing, and deep neural networks to interpret the text and provide suitable responses that feel human.
- Fraud detection : Machine learning helps flag stolen credit card use and detect illegal use of stolen or compromised financial data.
- Self-driving cars : ML enables self-driving vehicles to continuously identify objects in the environment around the vehicle. Use this identification to predict how they will move and guide the vehicle around objects while moving toward the driver's destination.
How to Learn Machine Learning
You can learn Machine Learning by enrolling at Stanford University for free. There are many courses online on platforms like Coursera and Udemy. You can also use EdX and Columbia University's Introduction to Machine Learning course. How to learn machine learning is now within the reach of many.
AI is the science of developing machines with reasoning and problem-solving abilities, allowing machines to learn and make decisions from past data without long and iterative programming. AI aims to create intelligent machines by combining machine learning and deep learning etc.
More and more technologies designed to allow developers to teach themselves about machine learning are coming out. We have AWS' deep-learning enabled camera DeepLens and others like Google's Raspberry Pi-powered AIY kits.
After going through this point you would have had your question “What is machine learning?” answered sufficiently.