Mathematical Pre-Requisites Required to Learn Machine Learning

By Jyoti Nigania |Email | Aug 23, 2018 | 19497 Views

The most important thing you must know if you want to get succeed as a Machine Learning engineer is how you should deal with the most precious thing called "DATA". Data analysis is the most important thing that you need to master in order to proceed with Machine learning. Although it may sound surprising, unless you are able to analyze the data correctly, you cannot build a model to use on the data. Now Data analysis is a pretty big field in itself and to work on data analysis.
In general, Machine Learning refers to the automated recognition of various patterns in large data. So, it's all about statistical analysis and that leads to design and implementation of effective algorithm following supervised, unsupervised or re-enforcement learning procedure. Machine learning in not something that you can get by just learning some mathematical concepts.
Linear Algebra used in Artificial Intelligence:
In Machine Learning or Deep learning or Artificial Intelligence all the primarily data build up is in the form of matrices and what does Linear algebra do Operations on matrices. Like transpose, multiplication, not only the matrix. Linear algebra involves while you are creating graphs like 2D, 3D and creating Vectors, creating Tensors.
In artificial intelligence we are constantly dealing with uncertainty. There is very little we can say for sure. Most of the time we have to settle for what is most likely and Probability theory is the Dominant framework for dealing with uncertainty.
In probability generally we deal with the following outcomes:
1) Impossible outcome
2) Unlikely outcome
3) Equally likely outcome
4) Certain outcome
5) The last that defines the artificial intelligence which is expected to come.
 Most of the machine learning algorithms used the concepts of Probability to create an analysis on the given data set and using the probabilistic analysis they create a prediction on the test data set to predict the target value.
Statistics: Artificial Intelligence is intrinsically data driven. The basic need to create an artificial intelligence model is Data and the initial data that we acquire from different sources is in the form of junk. To search the right data for our model we need to perform the Data Analysis on that for that process statistics turn to be pretty handy.
Calculus: We use calculus a lot in Artificial Intelligence. As artificial intelligence algorithms are nothing but simple mathematical functions. 
  • A step functions like ReLu, tanh.
  • An optimization functions like Gradient Descent.
  • Cost function.
All those functions are built on top of differential and integral equations. And all these calculus concepts act as electricity that powers artificial intelligence. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.The importance of machine learning can be understood when we realize how easily machine learning techniques can be used to solve problems that seem really hard eg., face recognition, you would realize that ML algorithms can tackle many seemingly difficult problems as long as there is enough data.

Source: HOB