Machine Learning — Post By Anjuum Khanna

The requirement of Machine Learning:-

Machine Learning is a part of Artificial Intelligence (AI). Applying AI, we wanted to build better and intelligent machines. But except for some small achievements such as finding the shortest path between point two points, we were unable to program more complex and constantly evolving challenges. So as we have learned that if there is a will there is a way, so we found out that the only way to be able to achieve this task was to let machine learn from itself and then should give output as required. This sounds similar to a child learning from its self. So machine learning was developed as a new capability for computers.

Types of Machine Learning:-

Supervised Learning Major part of machine learning is supervised learning. So in supervised learning machine learns from previous examples. So as per Anjuum Khanna’s simple definition in this learning, we provide both input and output data. This data use an algorithm to derive the mapping function from the input to the output.

What Machine Learning can do:-

I am amazed by seeing a change which is coming with artificial intelligence especially with machine learning. There are few areas where we use machine learning very often without even knowing that this is machine learning. Some of my favorite fields of machine learning are:-

Prerequisites to learn Machine Learning:-

Machine Learning requires a lot of logic and calculation hence mathematics is an important part of it. So it meets statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data which can be used to build intelligent applications.

  • Again for identification of underfitting and overfitting by following the Bias-Variance trade-off is required.
  • Estimating the right determination period and uncertainty.

So there are few basic topics on which an aspirant need to have a good grip. Those topics are:-

  1. Linear Algebra
  2. Probability Theory and Statistics
  3. Calculus
  4. Algorithms and Complex Optimisations

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