Machine Learning is an Application of Artificial Intelligence (AI) that gives devices the ability to learn from their experiences and improve their self without doing any coding. For example, when you shop from any website, it’s shows related searches such as: People who bought this, also bought this.
- What is Machine Learning?
- Why should we learn Machine Learning?
- How to get started with Machine Learning?
- The Seven Steps of Machine Learning
- How does Machine Learning work?
- Which Programming Language is best for Machine Learning?
- Difference between Machine Learning and Artificial Intelligence
- Types of Machine Learning
- Advantages and Disadvantages of Machine Learning
- Applications of Machine Learning
- Future of Machine Learning
- FAQs
What is Machine Learning?
Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”.
It is a subset of Artificial Intelligence. It is the study of making machines more human-like in their behavior and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming. The learning process is automated and improved based on the experiences of the machines throughout the process.
Good quality data is fed to the machines, and different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated.
Now you may wonder, how is it different from traditional programming? Well, in traditional programming, we would feed the input data and a well written and tested program into a machine to generate output. When it comes to machine learning, input data along with the output is fed into the machine during the learning phase, and it works out a program for itself. To understand this better, refer to the illustration below:
Why Should We Learn Machine Learning?
Machine Learning today has all the attention it needs. It can automate many tasks, especially the ones that only humans can perform with their innate intelligence. Replicating this intelligence to machines can be achieved only with the help of machine learning.
Businesses can automate routine tasks. It also helps in automating and quickly create models for data analysis. Various industries depend on vast quantities of data to optimize their operations and make intelligent decisions. It helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are precise and scalable and function with less turnaround time. By building such precise Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.
Image recognition, text generation, and many other use-cases are finding applications in the real world. This is increasing the scope for machine learning experts to shine as a sought after professionals.
How to get started with Machine Learning?
To get started, let’s take a look at some of the important
Terminology:
- Model: Also known as “hypothesis”, a machine learning model is the mathematical representation of a real-world process. A machine learning algorithm along with the training data builds a machine learning model.
- Feature: A feature is a measurable property or parameter of the data-set.
- Feature Vector: It is a set of multiple numeric features. We use it as an input to the machine learning model for training and prediction purposes.
- Training: An algorithm takes a set of data known as “training data” as input. The learning algorithm finds patterns in the input data and trains the model for expected results (target). The output of the training process is the machine learning model.
- Prediction: Once the machine learning model is ready, it can be fed with input data to provide a predicted output.
- Target (Label): The value that the machine learning model has to predict is called the target or label.
- Overfitting: When a massive amount of data trains a machine learning model, it tends to learn from the noise and inaccurate data entries. Here the model fails to characterize the data correctly.
- Underfitting: It is the scenario when the model fails to decipher the underlying trend in the input data. It destroys the accuracy of the machine learning model. In simple terms, the model or the algorithm does not fit the data well enough.
Here’s a video that describes step by step guide to approaching a Machine Learning problem with a beer and wine example:
There are Seven Steps of Machine Learning
- Gathering Data
- Preparing that data
- Choosing a model
- Training
- Evaluation
- Hyperparameter Tuning
- Prediction
It is mandatory to learn a programming language, preferably Python, along with the required analytical and mathematical knowledge. Here are the five mathematical areas that you need to brush up before jumping into solving Machine Learning problems:
- Linear algebra for data analysis: Scalars, Vectors, Matrices, and Tensors
- Mathematical Analysis: Derivatives and Gradients
- Probability theory and statistics for Machine Learning
- Multivariate Calculus
- Algorithms and Complex Optimizations
How does Machine Learning work?
The three major building blocks of a system are the model, the parameters, and the learner.
- Model is the system which makes predictions
- The parameters are the factors which are considered by the model to make predictions
- The learner makes the adjustments in the parameters and the model to align the predictions with the actual results
Let us build on the beer and wine example from above to understand how machine learning works. A machine learning model here has to predict if a drink is a beer or wine. The parameters selected are the color of the drink and the alcohol percentage. The first step is:
1. Learning from the training set
This involves taking a sample data set of several drinks for which the colour and alcohol percentage is specified. Now, we have to define the description of each classification, that is wine and beer, in terms of the value of parameters for each type. The model can use the description to decide if a new drink is a wine or beer.
You can represent the values of the parameters, ‘colour’ and ‘alcohol percentages’ as ‘x’ and ‘y’ respectively. Then (x,y) defines the parameters of each drink in the training data. This set of data is called a training set. These values, when plotted on a graph, present a hypothesis in the form of a line, a rectangle, or a polynomial that fits best to the desired results.
2. Measure error
Once the model is trained on a defined training set, it needs to be checked for discrepancies and errors. We use a fresh set of data to accomplish this task. The outcome of this test would be one of these four:
- True Positive: When the model predicts the condition when it is present
- True Negative: When the model does not predict a condition when it is absent
- False Positive: When the model predicts a condition when it is absent
- False Negative: When the model does not predict a condition when it is present
The sum of FP and FN is the total error in the model.
3. Manage Noise
For the sake of simplicity, we have considered only two parameters to approach a machine learning problem here that is the colour and alcohol percentage. But in reality, you will have to consider hundreds of parameters and a broad set of learning data to solve a machine learning problem.
- The hypothesis then created will have a lot more errors because of the noise. Noise is the unwanted anomalies that disguise the underlying relationship in the data set and weakens the learning process. Various reasons for this noise to occur are:
- Large training data set
- Errors in input data
- Data labelling errors
- Unobservable attributes that might affect the classification but are not considered in the training set due to lack of data
You can accept a certain degree of training error due to noise to keep the hypothesis as simple as possible.
4. Testing and Generalization
While it is possible for an algorithm or hypothesis to fit well to a training set, it might fail when applied to another set of data outside of the training set. Therefore, It is essential to figure out if the algorithm is fit for new data. Testing it with a set of new data is the way to judge this. Also, generalisation refers to how well the model predicts outcomes for a new set of data.
When we fit a hypothesis algorithm for maximum possible simplicity, it might have less error for the training data, but might have more significant error while processing new data. We call this is underfitting. On the other hand, if the hypothesis is too complicated to accommodate the best fit to the training result, it might not generalise well. This is the case of over-fitting. In either case, the results are fed back to train the model further.
Which Language is Best for Machine Learning?
Python is hands down the best programming language for Machine Learning applications due to the various benefits mentioned in the section below. Other programming languages that could be used are: R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala.
Python is famous for its readability and relatively lower complexity as compared to other programming languages. ML applications involve complex concepts like calculus and linear algebra which take a lot of effort and time to implement. Python helps in reducing this burden with quick implementation for the ML engineer to validate an idea. You can check out the Python Tutorial to get a basic understanding of the language. Another benefit of using Python is the pre-built libraries. There are different packages for a different type of applications, as mentioned below:
- Numpy, OpenCV, and Scikit are used when working with images
- NLTK along with Numpy and Scikit again when working with text
- Librosa for audio applications
- Matplotlib, Seaborn, and Scikit for data representation
- TensorFlow and Pytorch for Deep Learning applications
- Scipy for Scientific Computing
- Django for integrating web applications
- Pandas for high-level data structures and analysis
Here is a summary:
Difference Between Machine Learning and Artificial Intelligence
AI manages more comprehensive issues of automating a system utilizing fields such as cognitive science, image processing, machine learning, or neural networks for computerization.
On the other hand, ML influences a machine to gain and learn from the external environment. The external environment could be anything such as external storage devices, sensors, electronic segments among others.
Also, artificial intelligence enables machines and frameworks to think and do the tasks as humans do. While machine learning depends on the inputs provided or queries requested by users. The framework acts on the input by screening if it is available in the knowledge base and then provides output.
To understand both the terms with basic concepts you can take up below courses:
Introduction to Artificial Intelligence
Machine learning Course for Beginners
Types of Machine Learning
There are three main types:
Supervised learning: In this type, the model is trained on a labeled dataset, meaning that each example in the training data has a known label. The model can then make predictions on new, unlabeled data.
Unsupervised learning: In this type, the model is trained on an unlabeled dataset, meaning that the examples in the training data do not have known labels. The model can then find patterns or groups in the data.
Reinforcement learning: In this type, the model is trained by interacting with an environment where it receives rewards or punishments for its actions. The model can then be used to make decisions in new situations to maximize its rewards.
Advantages and Disadvantages
Everything comes with a few advantages and disadvantages. In this section, let’s talk about a few of the basic advantages and disadvantages of ML.
Advantages:
- It can be used for pattern detection.
- It can be used to make predictions about future data.
- It can be used to generate new features from data automatically.
- It can be used to cluster data automatically.
- It can be used to detect outliers in data automatically.
Disadvantages:
Some disadvantages include the potential for biased data, overfitting data, and lack of explainability.
Applications of Machine Learning
These algorithms help in building intelligent systems that can learn from their past experiences and historical data to give accurate results. Many industries are thus applying ML solutions to their business problems, or to create new and better products and services. Healthcare, defense, financial services, marketing, and security services, among others, make use of ML.
1. Facial recognition/Image recognition
The most common application is Facial Recognition, and the simplest example of this application is the iPhone. There are a lot of use-cases of facial recognition, mostly for security purposes like identifying criminals, searching for missing individuals, aid forensic investigations, etc. Intelligent marketing, diagnose diseases, track attendance in schools, are some other uses.
2. Automatic Speech Recognition
Abbreviated as ASR, automatic speech recognition is used to convert speech into digital text. Its applications lie in authenticating users based on their voice and performing tasks based on the human voice inputs. Speech patterns and vocabulary are fed into the system to train the model. Presently ASR systems find a wide variety of applications in the following domains:
- Medical Assistance
- Industrial Robotics
- Forensic and Law enforcement
- Defense & Aviation
- Telecommunications Industry
- Home Automation and Security Access Control
- I.T. and Consumer Electronics
3. Financial Services
Machine learning has many use cases in Financial Services. Machine Learning algorithms prove to be excellent at detecting frauds by monitoring activities of each user and assess that if an attempted activity is typical of that user or not. Financial monitoring to detect money laundering activities is also a critical security use case.
It also helps in making better trading decisions with the help of algorithms that can analyze thousands of data sources simultaneously. Credit scoring and underwriting are some of the other applications. The most common application in our day to day activities is the virtual personal assistants like Siri and Alexa.
4. Marketing and Sales
It is improving lead scoring algorithms by including various parameters such as website visits, emails opened, downloads, and clicks to score each lead. It also helps businesses to improve their dynamic pricing models by using regression techniques to make predictions.
Sentiment Analysis is another essential application to gauge consumer response to a specific product or a marketing initiative. Machine Learning for Computer Vision helps brands identify their products in images and videos online. These brands also use computer vision to measure the mentions that miss out on any relevant text. Chatbots are also becoming more responsive and intelligent.
5. Healthcare
A vital application is in the diagnosis of diseases and ailments, which are otherwise difficult to diagnose. Radiotherapy is also becoming better.
Early-stage drug discovery is another crucial application which involves technologies such as precision medicine and next-generation sequencing. Clinical trials cost a lot of time and money to complete and deliver results. Applying ML based predictive analytics could improve on these factors and give better results.
These technologies are also critical to make outbreak predictions. Scientists around the world are using ML technologies to predict epidemic outbreaks.
6. Recommendation Systems
Many businesses today use recommendation systems to effectively communicate with the users on their site. It can recommend relevant products, movies, web-series, songs, and much more. Most prominent use-cases of recommendation systems are e-commerce sites like Amazon, Flipkart, and many others, along with Spotify, Netflix, and other web-streaming channels.
Future of Machine Learning
The field will continue to evolve in the future; become sophisticated. It will be used in more areas of life and business, including healthcare, finance, and manufacturing. Machine learning will become more accessible to everyone, making it easier and more affordable. We have already seen the current applications of ML, with time and increased data and resources, the applications are only said to improve. ML Engineers are also in high demand, and professionals who learn the skill are being paid a generous salary. If you are interested in building a successful ML career, now is the right time to enter the field.
FAQs
1. What exactly is machine learning?
Arthur Samuel coined the term Machine Learning in 1959. He defined it as “The field of study that gives computers the capability to learn without being explicitly programmed”. It is a subset of Artificial Intelligence and it allows machines to learn from their experiences without any coding.
2. What is machine learning used for?
Machine Learning is used in our daily lives much more than we know it. These are areas where it is used:
- Facial Recognition
- Self-driving cars
- Virtual assistants
- Traffic Predictions
- Speech Recognition
- Online Fraud Detection
- Email Spam Filtering
- Product Recommendations
3. What is difference between machine learning and artificial intelligence?
A technology that enables a machine to stimulate human behavior to help in solving complex problems is known as Artificial Intelligence. Machine Learning is a subset of AI and allows machines to learn from past data and provide an accurate output. AI deals with unstructured as well as structured data. Whereas, Machine Learning deals with structured and semi-structured data.
4. How Machine Learning works?
The typical machine learning process involves three steps: Training, Validation, and Testing. The first step is to learn from the training set provided, the second step is to measure error, the third step involves managing noise and testing all the parameters. These are the basic steps followed and a very broad description on how it works.
5. What are the types of Machine Learning?
The broad types of machine learning are:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Semi-supervised Learning
- Reinforcement Learning
6. What is the best language for machine learning?
The best programming language to learn machine learning can be any of the following: Python, R, Java and JavaScript, Julia. However, in today’s day and age, Python is the most commonly used programming language due to it’s ease and simplicity. The number of programmers using Python as their primary coding language is increasing.
7. Is Alexa a machine learning?
Alexa is a virtual assistant that is created by Amazon and is also known as Amazon Alexa. This virtual assistant was created using machine learning and artificial intelligence technologies.
8. Is Siri a machine learning?
Similar to Alexa, Siri is also a virtual or a personal assistant. Siri was created by Apple and makes use of voice technology to perform certain actions. Siri also makes use of machine learning and deep learning to function.
9. Why is machine learning popular?
The amount of data available to us is constantly increasing. Machines make use of this data to learn and improve the results and outcomes provided to us. These outcomes can be extremely helpful in providing valuable insights and taking informed business decisions as well. It is constantly growing, and with that, the applications are growing as well. We make use of machine learning in our day-to-day life more than we know it. In the future, it is only said to grow further and help us. Thus, it is popular.