The idea of giving computers the capability to learn and solve problems on their own without being explicitly programmed, itself inspires a large group of people including me. This is what Machine Learning deals with. In this blog, we’ll get a basic understanding of machine learning and its wide range of applications in the present and future alike.
How does Machine Learning algorithms function?
Machine learning algorithms use historical data as input to predict new output values. But how do they do this? To understand this, we must first know the types of Machine Learning algorithms. Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.
Supervised Machine Learning
Supervised machine learning requires the data scientist to train the algorithm with both labeled inputs and desired outputs. Supervised learning algorithms are good for the following tasks:
- Binary classification: Dividing data into two categories.
- Multi-class classification: Choosing between more than two types of answers.
- Regression modeling: Predicting continuous values.
- Ensembling: Combining the predictions of multiple machine learning models to produce an accurate prediction.
Unsupervised Machine Learning
Unsupervised machine learning algorithms do not require data to be labeled. They examine through unlabeled data to look for patterns that can be used to group data points into subsets. Most types of deep learning, including neural networks, are unsupervised algorithms. Unsupervised learning algorithms are good for the following tasks:
- Clustering: Splitting the dataset into groups based on similarity.
- Anomaly detection: Identifying unusual data points in a data set.
- Association mining: Identifying sets of items in a data set that frequently occur together.
- Dimensionality reduction: Reducing the number of variables in a data set.
Semi-supervised Machine Learning
Semi-supervised learning works by data scientists feeding a small amount of labeled training data to an algorithm. From this, the algorithm learns the dimensions of the data set, which it can then apply to new, unlabeled data. The performance of algorithms typically improves when they train on labeled data sets. But labeling data can be time consuming and expensive. Semi-supervised learning strikes a middle ground between the performance of supervised learning and the efficiency of unsupervised learning. Some areas where semi-supervised learning is used include:
- Machine translation: Teaching algorithms to translate language based on less than a full dictionary of words.
- Fraud detection: Identifying cases of fraud when you only have a few positive examples.
- Labelling data: Algorithms trained on small data sets can learn to apply data labels to larger sets automatically.
Reinforcement Machine Learning
Reinforcement learning works by programming an algorithm with a distinct goal and a prescribed set of rules for accomplishing that goal. Data scientists also program the algorithm to seek positive rewards, which it receives when it performs an action that is beneficial toward the ultimate goal and avoid punishments, which it receives when it performs an action that gets it farther away from its ultimate goal. Reinforcement learning is often used in areas such as:
- Robotics: Robots can learn to perform tasks of the physical world using this technique.
- Video gameplay: Reinforcement learning has been used to teach bots to play a number of video games.
- Resource management: Given finite resources and a defined goal, reinforcement learning can help enterprises plan out how to allocate resources.
Scope of Machine Learning
Today, machine learning is used in a wide range of applications. In the future, it is destined to grow.
Fields in which Machine Learning is/can be implemented:
- Facebook, Google, YouTube, Netflix, Microsoft and other such tech oriented MNCs and startups uses machine learning to personalize how each member's feed is delivered.
- Machine learning algorithms can even make it possible for a semi-autonomous car to recognize a partially visible object and alert the driver.
- Sales and operations planning tools are unified dashboards for monitoring the activity in general and in detail. In other words, it is a secure system that uses data analytics on full scale.
- Machine learning can be used for Predictive Analytics in Stock Market Forecasting, Market Research and Fraud Prevention.
- It is used in Natural Language Processing i.e., Text Generation, Text Analysis, Text Translation and Chatbots.
- It is also used in Computer Vision for Image Recognition, Visual Search and Face Recognition.
- ML is also used in Banking and Personal Finance in the form of fraud prevention, credit decisions etc.
- One important field in which ML is becoming largely popular in recent times is Medical Diagnosis and Healthcare for early detection of infections from the patient’s symptoms.
These fields are only to name a few. There is much to explore the world with the knowledge of Machine Learning. If you find your calling in one of the above fields, then learning and implementing machine learning will surely give you an edge over your competitors.
Thanks for reading. Written by Syed Shahzad Abdul Wajid.