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With the growing technological advancement, machines are getting smarter, and more intelligent. Machine Learning is used in every aspect or field in our daily life. Be it predicting the weather, indicating the cost of flight/train tickets, in healthcare, which movie are you going to see next, and the list is never-ending. It is the most demanded technology of this era. If you are a beginner, who is thinking to get started with machine learning, you should be aware of the prerequisites of Machine Learning.
Statistics is a science concerned with collecting, presenting, giving insight into data, and interpreting data. Data plays a huge part in the field of machine learning. However, data without meaning is useless. There are two kinds of statistics:
a. Descriptive Statistics
b. Inferential Statistics
Descriptive Statistics: Descriptive Statistics is a tabular, graphical, and numerical summarize of data, i.e., it outlines the dataset into something meaningful.
Inferential Statistics: Inferential Statistics helps us test hypotheses or conclude sample data instead of whole datasets.
In Statistics, you should be familiar with:
Linear Algebra plays an integral part in the field of Machine Learning. It deals with vectors, matrices, and linear transformation. Furthermore, it is used to transform and perform operations on the dataset.
Concepts required in Linear Algebra are:
Words like “probably” and “likely or unlikely” we use in our day-to-day conversation are the basic understanding of Probability. In theory, an event(E) is a set of events, P(E) represents the probability of event E. For example, the event might happen (1) or might not occur (0). Probability is the foundation of machine learning. It helps us to predict an event to occur or predict the likelihood of incidents.
Concepts required in Probability are:
Calculus is an important field in mathematics and plays a vital role in machine learning. If we want to understand what is going inside the working algorithm, we should be familiar with calculus. The data set that we use in machine learning have multiple feature/attributes. It is used to build, train and test models.
Concepts required in Calculus are:
A good foundation of programming language can help you boost the skills required for machine learning. For example, a problem statement can be solved mathematically, but we need knowledge of programming language to implement it in real-life projects. Various programming language is present which can be used to implement machine learning algorithm.
Programming languages are:
Python is the most popular programming language for machine learning. It has easy syntax, pre-built functions, and packages are already present. We can access 235,000 packages and excellent community support.
R is another programming language that is widely used as much as python. Many applications are implemented through R. It has excellent library support.
C++ is majorly used for large systems and building games. It is used primarily for making libraries that we use in our python programming.
MATLAB comes with predefined functions and in addition, it has a GUI interface which makes it a lot easier to interact and work. It supports machine learning operations and is used in applications and computer vision.
Infrastructure, software, applications, and many other working parts of the company’s tech is already built upon Java. Popular frameworks and tools like Fink, Hadoop, Hive, Spark, and Weka used for Big Data are written in Java. Java Virtual Machine is the best platform for machine learning and data science for identical writing code across multiple platforms. It also allows them to create custom tools faster and features a load of IDEs that help improve overall productivity levels.
As a beginner, these are the essential prerequisites for getting started with machine learning. One should also know how to work with data. In this blog, we have only mentioned the requirement, along with the most preferred programming language. Paths can differ, but the goal remains the same. Machine learning keeps on changing and updating, so we need to be updated with the current market requirements.
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Feel free to drop any questions in the comment box, I would love to address them. I hope you enjoyed the article. Best of luck!
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