Prerequisites to Learn Artificial Intelligence and Machine Learning
Machine Learning (ML) and Artificial Intelligence (AI) are the most popular technologies in the 21st century. Most beginners and professionals want to make a career in these fields as both are the most lucrative fields of the computer science and engineering sector.
Artificial Intelligence (AI) is a field of computer science that deals with developing intelligent machines that can behave like humans, such as speech recognition, learning and planning, text recognition, etc. On the other hand, machine learning is a subset of artificial intelligence that enables the machines to use past data or experience and make a prediction and learn more accurately. Hence, both technologies are very much important to groom your skills and career in the current era. To do the same, you must know the primary requirements or prerequisites to enter in AI and ML fields. Let’s start with a quick introduction to AI and ML with important prerequisites.
What is Artificial Intelligence?
Artificial Intelligence is the branch of computer science and engineering that helps us to develop humans like intelligent computers or machines. It is a field of study where we learn how the human brain thinks, learn, decide and work to solve various problems and then, based on results; it develops intelligent software and systems.
Now, we will discuss some important prerequisites to learn Artificial Intelligence (AI). Here is a list of some prerequisites as follows:
Prerequisites to learn Artificial Intelligence (AI)
- Strong Knowledge of Mathematics: Before getting started with AI, you must have sound knowledge of various mathematical concepts such as probability, statistics, algebra, matrix, calculus, etc. Mathematics is very important to build logical capability that is widely used in developing software and systems.
- Good knowledge of Programming knowledge: To learn the fundamentals of writing codes, you must have sound knowledge of programming languages like Python, R, LISP, Java, C++, Prolog, etc.
- Strong Analytical skills: Analytical skills refer to the ability to think critically, analyze data, decision making capability as well as solve complex problems. These important skill sets involve taking in new information and mentally processing it in a productive manner. Hence, if you are planning to jump into the AI domain, you must build your analytical skills to a great extent.
- Ability to understand complex algorithms: Artificial Intelligence is a field that completely depends on various algorithms that tell computers how to learn and take actions further. There are a few important algorithms that you must know before getting started with AI as follows:
- Classification algorithms
- Regression algorithms
- Clustering algorithms
- Basic knowledge of Statistics and modelling: Statistical modelling is defined as the use of mathematical models and statistical assumptions to generate training data and predict outcomes for the future. A statistical model is a collection of probability distributions on a set of all possible outcomes of an experiment. We can say if anyone is looking to learn AI, then one must enhance statistics and modelling knowledge.
In this way, you are now aware of a few common prerequisites to learn Artificial Intelligence and ready to get started your career in this domain.
Now, we will discuss machine learning and important prerequisites to learning ML. So, let’s start with a quick introduction to Machine Learning technology.
What is Machine Learning?
Machine Learning is the branch of Artificial Intelligence that deals with enabling computers/machines to learn and predict results based on past experience or historical data without much human intervention.
If Artificial Intelligence helps in making intelligent system/software, then machine learning enable them to learn from available sample data and predict outcomes more accurately. Hence, we can say AI and ML are part of one another in different aspects.
Types of Machine Learning
Machine Learning is primarily categorized into 3 types. These are as follows:
- Supervised ML
- Unsupervised ML
- Reinforcement ML
Applications of Machine Learning
Machine Learning is one of the buzzwords of the 21st century. It is currently being used in several applications in different industries such as healthcare, medicine, transportation, social media, marketing, infrastructure, education, product recommendation, self-driving cars, chatbots, etc.
All small as well as large size organizations want to implement machine learning techniques in their business to grow more smartly than other competitors. Image recognition and personal virtual assistance such as Alexa, SIRI, Cortana are the most common examples of ML applications.
Machine Learning is a much-demanded technology in the IT sector. Most of the newcomers want to make a career in this domain. Besides fresher, there are so many experienced people who want to move to the ML industry to groom their skills and make a career in this domain. As machine learning is a very new technology in the IT sector, there are so many problems observed by ML experts, such as lack of knowledge, lack of trained resources, lack of experience, etc. However, organizations are continuously working to overcome these issues. Hence, if you are also planning to move your career in machine learning, then there are some key prerequisites that one should focus on firstly before getting started with ML.
Prerequisites to Learn Machine Learning (ML)
Since we have a basic understanding of Machine Learning and its associated concepts, now it turns out to know the primary requirements to learn ML. Below are a few prerequisites to get started with machine learning technology, which are as follows:
Strong Knowledge of Mathematics:
Similar to Artificial intelligence (AI), machine learning also requires in-depth knowledge of various mathematical concepts such as statistics, calculus, probability, and linear algebra.
This is one of the most important prerequisites to learning ML. If you have sound knowledge of mathematical concepts, you can easily build your own logic and implement them in developing intelligent software to predict accurately.
Good understanding of Programming Languages:
If you want to grow rapidly in this domain, then you must have a good understanding of programming languages such as Python, R, Java, C++, etc., to implement the process. Programming languages help you to perform a basic function such as:
- Defining and calling functions
- Collection of data
- Implementing loops with multiple variable iterators
- Implement various conditional statements such as if, if-else, etc.
- String formatting and passing statement, etc.
Hence, we can conclude if you are really planning to enter in ML domain, then you must go with at least one programming language given above. This will not only help you in learning ML but also help you in data modelling and analytics.
Strong knowledge of Data Analytics & Modeling:
Data modelling refers to the study of the structure of data sets to find hidden patterns inside them. Machine Learning is a technology that is completely based on the use of data and predictive data modelling. Hence, you must have a broad knowledge of data and its properties to identify the errors in the ML models.
Conclusion
Machine Learning and Artificial Intelligence are currently the most popular technologies, and in upcoming decades these technologies will be the core of the IT sector. As a prerequisite, both AI and ML technologies require a sound knowledge of basic mathematics concepts to implement in software or systems. You must have a good catch on statistics, linear algebra, matrix, calculus, probability, programming languages and data modelling. If you are confident in these areas, you can go ahead to make your career in these fields. In this topic, we have discussed a few important prerequisites to learn AI and ML. Hopefully, after reading this, you must have a clear understanding of the first step to entering this domain.