Machine Learning Introduction
Machine learning is in high demand in today’s technology-driven market. It is the latest trend that has taken the world by storm and has revolutionised the world of computer science. Additionally, the high amount of data produced by applications has led to a significant increase in computation power, resulting in the popularity and demand for machine learning skills amongst students and candidates.
Machine Learning is used across different fields. It has benefited industries and businesses by leaps and bounds, from automating basic tasks to offering valuable insights. Machine learning has been implemented in our day-to-day devices, such as fitness trackers, intelligent home assistants, healthcare systems, automated cars, and the like. Other essential examples where machine learning is implemented are:-
- Prediction: Machine learning has been majorly used in prediction systems useful for commuting fault probabilities before issuing a loan.
- Image recognition: Face detection and image detecting is a rage right now, and machine learning has made it possible.
- Speech Recognition: Similar to image recognition is speech recognition. It has been widely implemented in machine learning.
- Medical diagnoses: Machine learning has been implemented in healthcare technology to detect cancerous tissues.
- Financial industry and trading: Machine Learning has been widely used by companies for credit checks and for detecting frauds.
Machine learning or ML is an integral part of data analytics. It is used to create complex algorithms and models that have helped researchers, engineers, data scientists, and analysts forecast and deliver reliable information.
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History Of Machine Learning
‘Machine Learning’ was a term coined in 1959 by Arthur Samuel, a pioneer and expert in artificial intelligence and computer gaming. He defined it as the process that enables computers to learn without being programmed.
In the 1940s, the first computer system that could be manually operated was invented, known as ENIAC (Electronic Numerical Integrator and Computer). It was the inception of the idea of building a machine that could simulate human learning and thinking.
Because of statistics, machine learning was popularised in the 1990s and gave rise to probabilistic approaches in AI, which further shifted towards a data-driven approach. This paved the way for scientists to think about, design and build intelligent systems with analysing capabilities to learn from massive datasets.
Classification Of Machine Learning
Machine learning implementations can be separated into three different categories based on the learning “signal” or “response” that is available to a learning system. They are as follows:-
1. Supervised learning
When an algorithm uses example data and correlated target responses consisting of string labels or numeric values, like classes or tags, and learns how to predict the correct response later when they are given new examples, it is known as supervised learning. It is an approach that is akin to human learning under a teacher’s supervision, where the student memorises the good examples provided by the teacher. The student then makes out the general rules from these target examples.
2. Unsupervised learning
Unsupervised learning is when an algorithm learns from simple examples without any correlated response, leaving the determination of the data patterns on the algorithm alone. This algorithm usually restructures the data into something entirely different, like new features representing a class or a collection of un-associated values.
These are very useful in giving data analysts insights into the meaning of the data and offer valuable tips for improving supervised machine learning algorithms. It is almost akin to humans learning to determine that certain things or instances are from the same category by observing the similarity between two objects. Recommendation systems and ads that you come across by browsing the web are marketing automation and based on this kind of unsupervised automated learning.
3. Reinforcement learning
When an algorithm is presented with examples that do not have any labels, it can be classified as a type of unsupervised learning. However, when an example is accompanied by positive or negative feedback as per the solution proposed by the algorithm, it is reinforcement learning. This learning category is connected to the applications for which the algorithm is required to make decisions and bear the consequences.
It is similar to the trial and error method of learning in humans. Through the trial and error method, algorithms learn that specific courses of action are not as likely to succeed as others. One of the best examples to cite when it comes to reinforcement learning is when computers learn to play video games independently. The application gives the algorithm examples of certain instances or situations like having the player stuck in a maze while at the same time avoiding an enemy.
4. Semi-supervised learning
Semi-supervised learning is when an unfinished training signal is provided along with some missing target outputs. One of the exceptional cases of this principle is called Transduction, where the whole set of problem instances is determined at learning time, except for the part where the targets are missing.
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How does Machine Learning work?
Down below are the steps to understand how machine learning works:
- Data Gathering: First, past data in any form suitable for processing is gathered. The more the quality of data increases, the more appropriate it becomes for modelling.
- Data Processing: In most instances, data is collected in raw form and must be pre-processed. There can be several missing values for numerical attributes, like a house’s price can be replaced with the mean value of the attribute. However, the missing values for categorical traits can be replaced with the trait that has the highest mode. This depends on the kind of filters that are used.
- Divide the input data: The input data must be divided into training, cross-validation and test sets. The ratio amongst the sets is required to be 6:2:2
- Building models: Models should be built with suitable techniques and algorithms on the training set.
- Testing the conceptualised model: The conceptualised model is tested with data that was not fed to the model during the time of training and the evaluation of its performance with the help of metrics like F1 score, recall and precision.
Machine learning skills are among the top skills currently in demand in the job market because of the rising popularity and advancement of AI, which is an integral part of our lives now.
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What is the basic difference between ML and Traditional Programming?
In Traditional Programming DATA (Input) + PROGRAM (logic) is fed to the machine to run the program and achieve the output. On the other hand, in Machine Learning DATA(Input) + Output is fed to the machine to run it while training and the machine can create its program(logic), which is subject to evaluation while testing.
What are the prerequisites to learning ML?
The prerequisites to learn Machine Learning are Linear Algebra, Statistics and Probability, Calculus, Graph theory, and Programming Skills in languages such as Python, R, MATLAB, C++ or Octave.
How is data split in Machine Learning?
Data is split into three parts in Machine Learning. Training Data is required to train the model. This is the data that can be seen by the model actually from which it learns. Validation Data is used to quickly evaluate the model and has improved involved hyperparameters. Testing Data is thoroughly trained, and it provides an unbiased evaluation.