Machine Learning Tutorial

The field of study that deals with the capability of computers to learn and read without explicit programming is called machine learning. This is one of the most upcoming technologies on which a lot of other fields are dependent too. This study of computers and their programming is a lot similar to a person’s ability to learn. This field is one of the most active fields which are used by programmers and data scientists these days and is being used in a number of places too. In this article, we will talk about machine learning, the need to learn machine learning, its history, how machine learning works, its advantages, and its needs.

What is Machine Learning

The term machine learning was actually coined by a famous computer pioneer, Arthur Samuel. He initiated the idea that computers can also work and learn just like humans. The term machine learning can also be explained as improving the system of learning by computers that could depend on their older experiences without the actual programs or human guidance. This process initiates with training some good quality data with the help of computers and then building models on them that work on the basis of machine learning algorithms. The algorithm can be chosen depending on the data type or the type of task an individual wishes to perform using the specific model.

Machine learning is a very important skill that could be possessed by a data scientist or an analyst. It could also be developed by an individual who wishes to transform a huge amount of raw data into valuable predictions, graphs, or trends.

Let us understand this more with the help of an example below:

Real-Time Machine Learning Example

Let us assume that there are a few students in a class who actually want to understand a concept thoroughly while preparing for the exam rather than cramming the subject. For this to happen, the students feed their machines (aka brains) with all the data related to the subject. This can be called the process of training the machine where the students are training their brains with all the approaches and logic that could help them during the examination. Every time they take up a quiz or an assignment, they get a score or an accuracy measure that determines their performance depending on the score. With immense practice, the performance keeps on improving and the score gets better. 

The same way our computers work when the data is fed to them, they perform according to it. With continuous practice and improvements made to the model, the accuracy ratio increases every time. This is the main task to perform with every model where the data scientists work on the betterment of the model using various algorithms. 

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Why do we need machine learning

Machine learning is an evolving technology and is used by researchers and data scientists in a lot of fields. 

  • Machine learning is a need in today’s technology as it helps enterprises to have a clear picture of the trends in client behaviour as well as for operating business patterns.
  • It helps in the designing of new products.
  • Machine learning is the centre point for huge MNCs such as Facebook, Google, Uber, etc. 
  • It helps in saving both time and money by making humans work more efficiently and quickly.
  • Machine learning helps in automating the work that could be performed by a live agent. These tasks include work to change the password or checking the balance of an account.
  • ML is widely being used by researchers and healthcare experts in the healthcare industry. It helps in analysing the data points to suggest better outcomes by taking in all the previous data and working efficiently on it. 
  • Machine learning is helping in our daily routine by ensuring high security in routes, generating efficient ETAs, predicting when can a vehicle breakdown, etc.

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History of Machine Learning

The term machine learning was actually coined by a famous computer pioneer, Arthur Samuel. He initiated the idea that computers can also work and learn just like humans. The term machine learning can also be explained as improving the system of learning by computers that could be depending on its older experiences without the actual programs or we can say human guidance. This process initiates with training some good quality data with the help of computers and then building models on them that work on the basis of machine learning algorithms.

Then in the year 1949, a book was published named ‘The Organization of Behavior’ by the author Donald Hebb. This book contains theories that are based on behaviour related to the activity of the brain and neural networks. Thi became one of the most important pillars for strengthening this topic of machine learning.  would go on to become one of the monumental pillars of machine learning development.

Later, in the year 1950, researcher Alan Turing designed a Turing Test for determining if a computer possesses real intelligence or not. A computer was supposed to fool a human in order to pass the test. The idea to create this whole design was to get an answer to the question - can machines actually think?

How does Machine learning work?

Below are the steps for understanding the working of machine learning:

  • The user gathers all the data from the past in such a manner that it is suitable for processing. It works in such a way that the more suitable the data for processing, the better will be the performance of the model. 
  • Data Processing – There could be a scenario where the data collected by the user is not in a suitable form. Hence the process of data pre-processing is undergone. This may include the processes such as the conversion of data as if the data is present in the form of images or text, then it needs to be converted into numeric form. It could also be converted into the form of an array or a matrix. This process makes the data more consistent and efficient to use and it is more understandable for the machine as well.
  • The required ratio between the training and the cross-validation sets should always be 6:2:2.
  • The user can then build models with the help of suitable algorithms or techniques which are based on the training data or sets.
  • The process of testing the model with the help of the data which wasn't fed earlier to the model during the training or evaluation time. This process helps in figuring out the performance with the help of metrics like F1 score, recall, and precision.

Features of Machine Learning

Below are the features of machine learning:

  • It offers several tools which can help in predicting both structures as well as unstructured data using machine learning models and algorithms. This is known as automated data visualization.
  • The process of importing, processing, visualizing, modeling, and then evaluating the algorithm helps to get desired outcomes.
  • The processing of tasks properly using machine learning helps in increasing efficiency as well as effectiveness.
  • The customer engagement is phenomenal
  • It helps in getting improved accuracy in data analysis
  • ML has helped the people in achieving great results using intelligence networks with it.

Types of Machine Learning

Supervised learning: These algorithms are defined as that class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc. 

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For eg. to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.

Unsupervised learning: These algorithms are defined as that class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering: This means when the machine requires an inherent group while training the data.
  • Association: This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning. 

Reinforcement learning: This type of learning is based on feedback-type machine learning methods and this type does not require any kind of labelled data. An agent can perform in a certain way and then watches the reactions to his actions in this type of learning to judge the outcomes. Positive feedback is given for all the good actions however for all the bad actions, negative feedback is given. The agents are made to learn and understand using the experiences based on actions only, as there is no involvement of training data.

Semi-Supervised learning: This type of learning is a combination of both unsupervised learning and supervised learning. This type aims at overcoming the cons or drawbacks of both types. This is done by training the machine with both unlabelled as well as labelled data along with the examples.

Machine learning Life cycle

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  • Gathering Data: The first step of working with machine learning is to gather the raw data which is used as an input for model building. 
  • Data Preparation: The training data can be gathered from a number of places such as healthcare industries, IoT devices, hospitals, banks, etc. The final outcome can be used for the analysis of the model.
  • Data Wrangling: This stage includes removing the complex data and making it hassle-free. It is better and more convenient for a research analyst to use analysed and organised data in order to improve efficiency in the results. 
  • Analyse Data: The data has to be analysed before using it so that it is fit for further processes.
  • Train Model: In this stage, the model is trained using the input data. The main idea of training the model is to create an error-free algorithm based on it.
  • Test Model: After training the model using raw data, testing is done on the mode to get an outcome.
    Deployment: This step involves applying integrated machine learning models into processes to achieve proper functionality after deployment. 

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Machine Learning Architecture

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  • Data Acquisition: The initial step in the machine learning architecture is to define the data acquisition. This method involves the collection of data, preparing it, and then separating it depending on various case scenarios. We can also name this stage as pre-processing of data.
  • Data Processing: The acquired data needs to be processed now which involves processes such as data cleaning, encoding, and then transforming it into useful data.
  • Data Modelling: This method lets the user choose a specific algorithm for his data. An algorithm consists of a set of libraries which takes in data and the training process is done accordingly.
  • Execution: This is the testing stage which involves a lot of experiments on the model. This helps in making the final decision after the deployment.
    Deployment: The output gathered from the execution process is deployed for further process of exploration.

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Machine Learning Algorithms

As we have discussed in the section above, machine learning is divided into 4 main categories which are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These categories have a lot of algorithms under them which help in structuring the data in order to get better accuracy. 

A few important algorithms in machine learning are linear regression, decision trees, k-means algorithm, logistic regression, SVM algorithm, KNN algorithm, random forest algorithm, Naive Bayes algorithm, etc. The data can be collected from different sources which can be further used for analysis and are presented in a number of formats. The backbone of data science is machine learning. Data Scientists who want to work on these algorithms in data science need to have a good knowledge of machine learning along with a basic knowledge of statistics.

Application of Machine Learning

  • Social Media: Machine learning algorithms are used by social media platforms for creating attractive features. For example, the Facebook app notices the activities are past records of the users such as their likes, their interests, chats, and their engagement on a specific type of post. We start getting the same kind of suggestions according to our interests as we browse further. This is because machine learning algorithms as models are trained in a way to understand the interest of humans in a particular field. 
  • Automation: Researchers have always been working on automation for a long period of time. But machine learning has made its way successful as people are now able to automate the platforms easily, which do not even require the efforts of humans. We can now easily train the machine using the previous data and the process can be put on automation. 
  • Finance Industry: ML plays a very important role in the finance sector as it helps in managing various financial applications and services such as management of assets, handling the risk levels at various stages, calculating the credit scores, approving financial loads, etc. Machine learning is simply a part of data science that helps in providing the capability to understand and learn just by improving the experience, making it easy and better.
  • Government organisation: Machine Learning acts as a very powerful aspect as its deep learning algorithms help in the identification of criminals using revolutionised image detection methods and classification. Not just this, ML also helps in the prediction of traffic contributing to the government sector.
    Email Spam: When a user receives an email, it automatically gets filtered into the inbox, as important, spam, normal, etc. The important email always goes into the inbox and the unimportant ones are filtered in the spam folder. The innovation of machine learning algorithms plays a major role in this as the models are trained to filter the emails depending on their type. 
  • Healthcare industry: Machine learning algorithms are used in the healthcare industry to diagnose diseases. Researchers are now able to build 3D models in this fast-growing industry in the medical field. This helps them to get better results and detect the actual portion of the issue. Machine learning is also helping industry researchers to easily detect brain tumours or even various brain-related problems.

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Conclusion

In this article, we have talked about the basic introduction of machine learning concepts and the algorithms affiliated with it. The field of study that deals with the capability of computers to learn and read without explicit programming is called machine learning. We have also discussed the types of machine learning, the benefits of using machine learning, its applications, architecture, and its features. Machine learning is making our lives easy and efficient in an unknowing manner.

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Saritha Reddy
Saritha Reddy
Research Analyst
A technical lead content writer in HKR Trainings with an expertise in delivering content on the market demanding technologies like Networking, Storage & Virtualization,Cyber Security & SIEM Tools, Server Administration, Operating System & Administration, IAM Tools, Cloud Computing, etc. She does a great job in creating wonderful content for the users and always keeps updated with the latest trends in the market. To know more information connect her on Linkedin, Twitter, and Facebook.