Machine learning (ML) is an application of Artificial Intelligence (AI) that allows systems to automatically learn and improve from experience without being explicitly programmed for the task. This can be described as machine learning focuses on developing programs that can access data and use it to learn for themselves.
Machine learning is an important component of the growing field of data science and machine learning represents a major step forward in how computers can learn.
In this article we will try to give a simple introduction for those who want to understand machine learning.
- What is Machine Learning?
- Why use Machine Learning?
- How to use Machine Learning?
- What Skills Do You Need to Use Machine Learning?
- Machine Learning and Artificial Intelligence (AI): What’s the difference?
- Use Cases for Machine Learning: Examples of Applications
- Most Used Machine Learning Tools
- Challenges with Machine Learning
- Summary: Machine Learning Infographic
- FAQ: Machine Learning Introduction for Beginners
What is Machine Learning?
Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from experience without being explicitly programmed for the task.
Machine learning can be broadly defined as the capability of a machine to imitate intelligent human behaviour.
What this means is that machine learning focuses on developing programs that can access data and use it to learn for themselves.
Why use Machine Learning?
There are several reasons to use Machine Learning, including:
- Machine learning enables analysis of massive amounts of data.
- The main objective of Machine Learning is to allow the program to learn automatically without human intervention or assistance and alter activities accordingly
- Machine learning is an important component of the growing field of data science. Within Data Science, you use statistical methods and machine learning algorithms that are trained to make classifications or predictions with the purpose of uncovering key insights within data
- For companies that have data in their core strategy, and depend on large quantities of data, need a way to efficiently and accurately analyse and make use of that data. Machine Learning is one the best ways to build models, strategise, and plan
- Machine learning is the core of some companies’ business models, like in the case of Netflix’s suggestions algorithm or Google’s search engine. The practical applications of machine learning can drive positive business results
How to use Machine Learning?
Machine Learning algorithms can broadly be divided into three subtypes:
Uses pre-labeled data to train models. It means some data is already tagged with the correct answer. The machine has a “supervisor”, or a “teacher”, who gives the machine all the answers, like whether it’s a cat in the picture or a dog
You do not need to supervise the model; instead, the model will work on its own to discover information. Unsupervised learning means the machine is left on its own with, for example, a set of animal photos and a job to find out and categorize between the different animals
As you may have noticed, the main distinction between the supervised and unsupervised learning is the use of labeled datasets.
I really like this visualisation from machine learning for everyone that show the difference between supervised and unsupervised learning
And third and final, we have what is called
To get the program to do what we want, the machine learning model gets either a reward or a penalty for the actions it performs. Its goal is to maximise the total reward. This means that reinforcement learning is reward-based learning which works on the system of feedback
What Skills Do You Need to Use Machine Learning?
Machine learning engineering uses software engineering concepts with analytical and data science.
- Data Science skills: The data science concepts that machine learning engineers use include: understanding of programming languages such as Python, SQL, and Java, hypothesis testing, proficiency in mathematics, probability, and statistics. More about technical skills in data science in our post: Technical Skills to become a Data Scientist
- Software Engineering skills: Some of the base concepts that machine learning engineers rely on include: writing and structuring algorithms, understanding data structures, and knowledge of computer architecture
- Additional skills: Machine Learning professionals also utilise deep learning, dynamic programming, natural language processing, audio and video processing, and reinforcement learning, among others
Machine Learning and Artificial Intelligence (AI): What’s the difference?
First, in short regarding Artificial intelligence (AI). AI refers to a computer system’s capacity to simulate human cognitive capabilities such as learning and problem-solving. A computer system that employs AI combines arithmetic and logic to imitate the reasoning that humans use to learn from new information and make decisions.
Is perhaps artificial intelligence and machine learning the same? Well, while AI and Machine Learning are very closely connected, they’re not the same. Machine learning is regarded as a subset of AI. Thus, Machine learning is an application of AI.
On a general level, we can differentiate AI and machine learning as:
So when looking at AI vs Machine Learning, you could say that you’re looking at their interrelationship.
Use Cases for Machine Learning: Examples of Applications
To give a short introduction with a few examples of some use cases for machine learning
Machine Learning in Finance
Finance is one of the most critical sectors in the world, and with the use of machine learning, companies can now quickly analyse financial related matters and make better decisions.
Machine Learning has a wide range of applications and use areas in the financial sector, to name a few:
- Fraud Detection: With the help of machine learning algorithms, companies can analyse big data and detect anomalies with higher precision and speed. The machine learning application helps with fraud detection for safe transactions
- Algorithmic Trading: By implementing smart machine learning applications, financial institutions can get a better understanding and make better predictions on their algorithmic trading. The machine learning algorithm is learning to make better trades.
- Process Automation: Machine Learning solutions allow finance companies to replace manual work by automating repetitive tasks through intelligent process automation. For example chatbots and paperwork automation are two examples of process automation in finance using machine learning. This application is of course useful in a wide range of industries, not only finance
Machine Learning in Healthcare
Machine Learning in healthcare is very beneficial as machine learning was developed to deal with large data sets, and patient files are exactly that as it includes many data points that need thorough analysis and organising.
Some examples of use cases for machine learning in healthcare are:
- Computer Assisted Diagnosis (CAD): Machine learning algorithms can help to determine and label the kind of disease or medical case that the medical staff are dealing with
- Make Recommendations: Machine learning algorithms can advise and give medical information without the need to actively search for it. In other words, the application recognises patterns and can give recommendations for a patient. The system uses the patient history and can produce multiple potential treatment options.
- Predictive Approach to Treatment: Machine learning in healthcare can be used to successfully predict diseases and give patients a chance of starting the treatment early, being predictive. For example, signs of diabetes can be predicted using a machine learning algorithm
Machine Learning for Online Sales and Marketing
The better you can understand your customers, the better you can meet their demands, and the more you will sell. In online marketing and e-commerce, marketers use machine learning to find patterns in user activities on a website.
Some examples of machine learning for marketing include:
- Make personalised product recommendations: Popular eCommerce giants like Amazon and Netflix are using machine learning algorithms to achieve it. For example, if you scroll through Amazon you will notice that it can give you quite sophisticated recommendations on other products you might like.
- Forecast Targeting: Predictive forecasting machine learning makes forecasts using various data sources, including sales history, customer searches, economic indicators, and demographic data
- Identifying Styles of Popular Products and Predicting Trends: Machine learning applications can support in identifying customer behaviour and shopping patterns. This is crucial as it helps marketers to understand what impacts consumers’ buying decisions
Machine Learning for Self-Driving Cars
Much of the technology behind self-driving cars is based on machine learning, deep learning in particular. The development of self-driving cars is one of the most trendy and popular directions in the world of machine learning.
Self-driving cars are made possible by machine learning algorithms as they make it possible for a vehicle to collect data from cameras and other sensors and then interpret it and decide the following actions to perform.
Most Used Machine Learning Tools
There are several Machine Learning tools that are available in the market, below are some of the most used Cloud Services and Platforms, and Programming Libraries and Frameworks
Cloud Services and Platforms
Microsoft Azure Machine Learning
IBM Watson Machine Learning
Amazon Machine Learning
Google Cloud Machine Learning
Programming Libraries and Frameworks
Read more about TensorFlow in our post on Python Top 10 Libraries
Apache Spark MLib
Challenges with Machine Learning
The most important task you need to do in the machine learning process is to train the algorithm with sufficient and valid data to achieve an accurate output.
Therefore, some of the major challenges that you might face while developing your machine learning model include:
Poor Quality of Data
Data plays a significant role in the machine learning process. Unclean and noisy data can make the whole process extremely difficult and cause our algorithm to make inaccurate or faulty predictions.
Therefore, data quality is essential to improve the output and ensure our machine learning program can train and learn from the right data.
Not Enough Training Data
It generally takes a considerable amount of data for most algorithms to function correctly. Less amount of training data will create imprecise or too biased predictions.
A rule of thumb could be that a simple task needs thousands of examples to make something out of it, and for advanced tasks like image or speech recognition, it may need millions of examples.
Feature Selection is one of the core concepts in machine learning that impacts your model’s performance. Therefore, irrelevant or partially relevant features can negatively impact model performance.
So basically, feature selection is the process where you automatically or manually select the features in your data set that contribute most to your prediction variable or output in which you are interested in.
Overfitting of Training Data
Overfitting refers to a model that models the training data too well. This happens when a model learns the detail and so-called noise in the training data to the extent that it negatively impacts the model’s performance on new real-life data.
Why is this a problem? Well, the noise or random occasions (outliers etc.) in the training set will be learned as concepts by the model, and it will try to execute these concepts on the new data set.
Underfitting of Training Data
Last, but definitely not least, underfitting of data happens when the data is unable to establish an accurate connection between input and output variables. This means that the data is too simple to establish a precise relationship.
However, a good thing is that it is often quite easy to detect given a good performance metric.
Machine learning is a complex process and Imperfections in the algorithm when data grows or the process is transforming could occur. Hence there are chances of error which makes the learning complex. You need regular monitoring and maintenance to keep the algorithm working.
Underfitting vs. Overfitting of training data
A rule of thumb is that the model is underfitting the training data when the models perform badly on the training set due to the fact that the model is incapable of finding and learning the relationship between the input (often our X) and the target value (often our Y)
Summary: Machine Learning Infographic
Let’s summarise some of the key points that we have looked at in this post in an infographic. Please feel free to save it for later use and share it with friends and colleagues.
FAQ: Machine Learning Introduction for Beginners
What is Machine Learning?
Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from experience without being explicitly programmed for the task. This can be described as machine learning focuses on developing programs that can access data and use it to learn for themselves.
What is the difference between AI and machine learning?
Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. AI on the other hand is a wider concept to create intelligent machines that can simulate human thinking capability and behaviour
What are the three types of machine learning?
There are three types of machine learning algorithms:
1. Supervised learning: The model has a “supervisor”, or a “teacher”, who gives all the answers
2. Unsupervised learning: The model will work on its own to discover information and find patterns
3. Reinforced learning: The model gets either a reward or a penalty for the actions it performs
How is machine learning being used?
There are numerous use areas for machine learning. Some examples are:
• Self-driving vehicles: The model can Interpret the data and decide the following actions to perform
• Healthcare: For example Computer Assisted Diagnosis (CAD) and make predictions for treatment
• Finance: Fraud Detection, Algorithmic trading, process automation, etc
• Marketing: Make personalised product recommendations and identifying trends