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Prerequisite for Machine Learning: It’s Not What You Think It Is

Currently, Machine Learning is one of the most sought after technology. If you’re a novice in this subject, then you must know the prerequisites for Machine Learning. Before getting started, it’s important you understand different concepts and different types of machine learning that are going to help you out in this field.

The following article will talk about what machine learning is and its various types. It will also shed some light on the various machine learning prerequisites and the growing importance of machine learning in today’s world. 

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence and is the scientific study of algorithms and statistical models used by computer systems. They use it further to perform a specific task with the help of patterns and inference of data.

The primary aim is to allow computers to learn automatically, with no human intervention or assistance. It should also be able to adjust and adapt to actions accordingly. 

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Machine learning prerequisites: Types Of Machine Learning

Before delving into the prerequisite for machine learning, let’s first look at what are the various types of machine learning algorithms used commonly by data scientists. To put it simply, there are four main types of machine learning; supervised, unsupervised, semi-supervised, and reinforcement learning. The kind of data you want to predict automatically determines which approach you will select from the above-mentioned types.

  • Supervised Machine Learning- This type of machine learning deals with machines that are usually trained using well-labeled training data. Based on this data, the machines can then detect the output. In this case, both the input and output of the algorithm are already specified. 
  • Unsupervised Machine Learning- Contrary to supervised machine learning, this type of algorithm works with unlabeled data. It searches for any kind of meaningful connections through the dataset. 
  • Semi-supervised Learning- To put it simply, semi-supervised learning is a mixture of both unsupervised machine learning and supervised machine learning. So there are instances that might work in this scenario. For example, data scientists can use well-labeled training data for the algorithm, or the algorithm can also explore the data set on its own and search for connections accordingly. 
  • Reinforcement learning- Last but not least is reinforcement learning, which basically means taking the necessary actions to maximize the output or reward. This is done with the help of various software or tools that search for the best route available for a particular task or situation. 

Why is Machine Learning important?

In today’s world of technology, various big enterprises such as Facebook, Uber, and even Google have started implementing Machine learning in their business operations. It has undoubtedly become one of the most important ways always to stay one step ahead of the competition. Several practical applications of machine learning, such as time and money saving, play a huge role in the sustenance of your business in the near future. Perhaps, one of the best examples of this application of machine learning and how it benefits various sectors can be witnessed in customer service.

The use of machine learning in customer service has led to a much better customer experience and, ultimately, a huge profit for the company. Gone are those days when we relied on the traditional methods of business operations, as now simple tasks can be performed much more quickly and efficiently with the help of this technology, and knowing the prerequisites for machine learning can significantly simplify the process. 

With that said, here are some of the applications of machine learning highlighted in the list below, which also includes the various prerequisites of machine learning.

Applications of Machine Learning

We are moving towards automation and artificial intelligence to be more efficient. Therefore, there is a lot of scope in terms of Machine Learning and its applications. 

Here are a few of them:

1. Image Recognition

One of the most common uses of Machine Learning is when it implied for face detection in an image. There is a separate category for each individual in a database. You can also use Machine Learning for character recognition for handwriting or printed letters.  

2. Medical Diagnosis

It can be used in techniques and tools that are going to help in the diagnosis of diseases. With the help of analysis of clinical parameters, prediction of disease progression is made. From here, you can have a medical opinion in terms of the therapy planning of the patient, along with monitoring.

3. Financial Sector

Machine Learning is the driving force for the popularity of services that the financial sector provides. It helps banks and other institutions to make smarter decisions. With the help of Machine Learning, you can predict an account closure beforehand.  

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Prerequisite for Machine Learning

Knowing the prerequisites for machine learning demands a certain level of skill set proficiency Since we now have a better understanding, we can talk about Machine Learning prerequisites:

  1. Statistics, Calculus, Linear Algebra and Probability

  1. A) Statistics is one of the most important prerequisite for machine learning. Statistics contain tools that are used to get an outcome from data. 
  • Transforming raw data into valuable information, descriptive statistics are used.
  • Inferential statistics are used to get information from a sample of data without using the complete data set.

When it comes to prerequisites to learn Machine Learning, this is high up on the list, as it does involve some basic maths. This lays down the core foundation of how information can be extracted from data at hand.

    B) Speaking of mathematics, Calculus also is a prerequisite of Machine Learning, and it plays an integral role in the algorithm. As data sets with multiple features are used to build learning models. Multivariable calculus plays a vital role in building a model of machine learning.

    C) Linear Algebra is dealing with matrices, vectors, and linear transformations. It is used in machine learning to perform operations and transform on datasets.

    D) As probability is used for prediction of the occurrence of an event, it helps you to reason the situation – as to why a certain event took place. Probability is a foundation in machine learning prerequisites.  

  2. Programming Knowledge

Being able to write code is one of the most important things when it comes to Machine Learning. You need to know languages such as Python and R to implement the process. 

Basic functions such as:

  • Defining and calling functions
  • Lists, sets, and dictionaries (assessing, iterating and creating)
  • for loops with multiple variable iterators
  • if/else conditional expressions
  • String formatting
  • Pass statement – for syntax

You should do a course in Python, to be specific. This will not only ease your process of learning this subject but also give a better understanding of data modeling.

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  3. Data Modeling

It is a process of estimating the structure of the data set, and it is done to find any variations or patterns within. Machine Learning is also based on predictive modeling. Therefore, you need to know various properties of the data you have, in order to predict.

Learning iterative algorithms can result in errors in the set and model — a deeper understanding of how data modeling functions is a necessity. 

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Conclusion

We focused on the prerequisites of machine learning in this article, and its applications as well. You need to have some understanding of maths – statistics, probability, linear algebra, and calculus, programming language, and data modeling. 

Machine Learning is a lucrative career to get into, but it requires a certain amount of practice and experience. It’s not a quest that can be done overnight. But if you have a look at machine learning salaries, then you will find the effort worth.

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Why study machine learning?

With every organization and every industry striving to employ AI and its advanced technologies in their domain, it is easily understood that machine learning is the star of the hour. Learning machine learning can help open up never-ending opportunities for you to shape out a long and highly rewarding career. You can work in projects that develop sophisticated machine learning applications for image recognition, cyber security, healthcare, medicine, and much more. Reports suggest that by the year 2026, the market for MLaaS, i.e., Machine Learning as a Service, is estimated to reach almost 12.1 billion USD.

What are some of the most popular machine learning jobs?

As the world rushes ahead to embrace artificial intelligence and top emerging technologies, the market for machine learning keeps expanding exponentially. Consequently, the demand for professionals trained in machine learning and who have relevant experience also keeps rising. The world's top technology organizations are always scouting for the best talents in machine learning. Some of the most in-demand jobs in this field today are those of data scientist, machine learning engineer, cyber-security analyst, computational linguist, cloud architect for machine learning, robotics engineer, designer, or researcher in human-centered AI systems. Moreover, there are lucrative non-technical jobs like AI ethicist, data lawyer, and conversation design specialists or experts.

How much does a machine learning engineer at Google earn?

Typically, the compensation of a machine learning engineer working with tech giant Google ranges around $143,050 a year on average. The average range of machine learning engineer salaries at Google is $73,000 to $315,000 a year. As per data obtained from glassdoor.com, when factors such as additional compensation components and bonuses are considered, the average earnings of a machine learning engineer at Google can also be around $153,300 a year. However, it is mention-worthy that the average pay depends on several factors such as education, certifications, location, and overall work experience.

What are some tips to keep in mind before I start machine learning?

Machine learning is a subset of artificial intelligence. It covers techniques that allow computers to learn from experience. The basic premise is that you can write a computer program that will automatically improve its performance at a task as it is exposed to data. The important thing to be aware of is that machine learning is not magic; you can't expect to be able to throw huge amounts of data at a machine learning algorithm and expect it to magically make accurate predictions. You have to use the right tool for the right job, and learn the basics of the algorithms so you can figure out what tool to use.

What is a decision tree in machine learning?

A decision tree is a flowchart-like structure that is used to make decisions. These are often used in classification problems, where the goal is to categorize objects into one of several classes. The decision tree learns from sample inputs, and at each point in the tree, it tests the values of a given number of features. If a feature tests positive, then the value of that feature is used to decide which branch in the tree to take. Decision tree is one of the most commonly used techniques for classification and regression problems, and can be easily visualized.

What is supervised learning in machine learning?

Supervised learning is the process by which an algorithm analyzes a set of data that has been input by providing the correct answers, and then makes predictions based on new, unseen data. It is one of the two main categories of machine learning. Unsupervised learning, on the other hand, is the process by which an algorithm analyzes data without being told correct answers beforehand. The data processed in unsupervised machine-learning algorithms is typically not labeled, but rather the algorithm itself finds patterns in the data and organizes it into clusters or classifies it into categories.

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