Introduction to Machine Learning In a Simple & Less Technical Way
Machine Learning (ML) is a subfield of Artificial Intelligence that can help us to generate difficult and complex algorithms.
We, developers, write algorithms that can take inputs and produce the desired output. It means we have the input data and the algorithm to do the job.
In machine learning the above’s idea is excitingly different. In machine learning, we don’t have to write that complex algorithm ourselves instead we give the sample input and the correct sample output data to an artificial brain called the Neural Network so it can train itself and act as that algorithm for us. So here we have the input and output data and machine learning has helped develop the required algorithm.
Once our neural network is trained, we can give it the input data and get the expected output.
For example, we can take a few hundred images of apples and bananas along with their corresponding labels. The images are our input data and the labels are our output data. Our task here will be to give our input data which are pictures of apples and bananas to the neural networks so they can understand the structure and patterns of images and understand their relationship to their labels and learn to give the correct label as output. For apples, it should give us the number 0, and for banana number 1.
Note that we have used numbers 0 and 1 for classifying images instead of strings like “banana” and “apple” because neural networks operate on numerical data only.
In machine learning, we don’t say to train the neural network directly, instead, we refer to it as a Model which is a container that encapsulates the neural network along with other information and configuration which are required for its operation.
This is the core mental model needed to get started with machine learning and to grasp the basic concepts which are discussed below.
A. Dataset
Dataset refers to the collection of input and output data in an ML-friendly format. Data is the first requirement in machine learning for training a model which is to say training the networks. An accurate and large dataset helps in training an accurate model.
The dataset is mostly divided into two sets a training set and another test set. The training set is used to train the model while the test set is used to test the trained model immediately for evaluation purposes.
B. Techniques
Techniques refer to the strategy we can implement to train our model with the given dataset. Techniques are mostly relative to the kind of dataset we have. There are four techniques mainly.
1. Supervised Learning
When we have the input data and the output data for training the model we call it supervised learning. It is the most common technique used in the community for getting accurate predictions.
2. Unsupervised Learning
When we only have the input data but not the output data this is referred to as unsupervised learning. This is a less practical technique and is mostly used where the correctness of the prediction is not necessary. The idea is to make the networks learn about the structure and patterns of the input data.
3. Semi-Supervised Learning
When there is no output data for every input data then it is called semi-supervised learning. This technique takes advantage of both supervised and unsupervised learning where the input data with corresponding output data helps the model to understand the relationship between them and the input data with no corresponding output data helps the model in understanding the hidden patterns and structure of input data.
4. Reinforcement Learning
Reinforcement learning is a different one than discussed above. It is a feedback-based machine learning technique where computer programs explore the environments and take action according to their experience. They receive a positive reward for every good action and a negative reward for every bad action. The goal of this technique is to maximize the positive rewards.
Reinforcement learning is mostly implemented in Robotics and Automation Industries such as self-driving cars, automated medical diagnosis, and training robots.
C. Model Evaluation
After training the model, evaluating it is crucial to get an idea of how it will perform on unseen input data. Here we utilize the testing set of the dataset to evaluate the trained model.
D. Overfitting and Underfitting
Cases where the trained model may not give the expected results.
1. Overfitting
If the model has performed well on the training dataset but fails to predict accurate results on unseen data, this is the case of overfitting where the model has failed to learn the actual relationship between input and output data.
2. Underfitting
When the model has failed to perform well on training and test sets then it is considered as the case of underfitting. Here the model might be too simple for capturing the underlying patterns and structure of the data and might need improvement.
E. Deployment
After the training and evaluation of the model, it can be saved and deployed to production and make predictions on unseen data. Deployment involves integrating it into the application, setting up the APIs, and building the interface to let real-world users interact with the model.
Applications of Machine Learning
Machine learning is being used in almost every sector now, e.g. e-commerce, marketing, healthcare, finance, automation, etc. It has a wide range of applications across various domains.
It’s important to learn about the application areas of machine learning so we can get an idea of why it is becoming an undistinguishable part of the tech industry.
Image and Object Recognition
Machine learning is being used to classify and recognize/detect objects within images and videos. E.g. in surveillance systems, it is being used to track human movement, in autonomous vehicles, it is being used to detect other vehicles and humans, in the facial recognition field it is being used to identify human faces, and in medical imaging, it is being used to effectively analyze the health condition.
Natural Language Processing (NLP)
Machine learning helps machines to interpret and understand human language. Applications with NLP include features such as speech recognition, chatbots(ChatGPT, Bard), text generation, language translation, etc. Because of the recent AI Boom NLP is the easy to understand field.
Fraud Detection
Machine learning helps in identifying fraud and cyber threats. It can analyze patterns and detect abnormalities in the activities of the user. This can help in avoiding credit card fraud, insurance fraud, identity theft, and cybersecurity threats, and help maintain secure applications.
Autonomous Systems
Machine learning is used to develop autonomous systems such as self-driving cars, drones, and smart home devices.
Financial Analysis
Machine learning is being used to identify the patterns of financial market data and make predictions, helping investors and financers to make better decisions.
Recommender Systems
Machine learning can be used to make a recommendation system based on user’s preferences, behaviors, and activities. E.g. recommendations for movies, commercial products, music, and digital content.
Healthcare and Medicines
Machine learning has a massive adoption in the medical sector. It is being used to analyze medical imaging, disease diagnosis, drug discovery, personalized medicine, and patient monitoring.
Predictive Analytics
Machine learning can be used to train machines to predict results based on historical data making it suitable for financial markets, sales forecasting, and customer behavior analysis.
Summary
Machine learning is the field of teaching artificial brains to do complex jobs which can or cannot be done by human brains. The point here is that the human brain cannot be available 24/7 for the service without getting tired neither it can read thousands of questions and answer them simultaneously and nor a human can walk on the surface of Mars without being familiar with the environment.
This is the reason for such heavy adoption of Artificial Intelligence in every sector.
Thank You for Reading.
