Prerequisites of Machine Learning: Kickstart your Data Science Journey



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Prerequisites of Machine Learning: Kickstart your Data Science Journey

When we talk about machine learning, it always does not need to refer to complicated algorithms and equations. Well, these skills can be developed by some basic building blocks. So, today we are going to talk about the prerequisites to kickstart your journey in the diverse world of machine learning.

With the advancement of Computer science, the world is getting enriched with more and more data. Now, drawing useful inferences from this huge pool of data can be a hard job to accomplish with. After all, the pattern in the data is what decided what to interpret from that. So, in that regard, a machine can be obviously more accurate to find the pattern in the data, rather than doing it manually. This is what a machine is groomed to accomplish in the world of data science.

Machine learning is one of the most significant fields of data science and a subset of artificial intelligence. It uses statistical and mathematical models to structure algorithm and fit in the data to find patterns. From image recognition to prediction models, most of them are gifts of machine learning.

Prerequisites

1. Mathematics and Statistics: Speaking about data science cannot be skipped without the involvement of mathematics. The world of data science is built on the underlying concepts of nothing but mathematics and statistics. These concepts act as the backbone of those rigorous algorithms. So, it is an absolute necessity to possess specific constructive knowledge of these two subjects.

Linear Algebra: It deals with matrices, determinants, vector spaces and linear transformations. Linear algebra is probably the most crucial section of mathematics to conquer for starting with machine learning.

Differential Calculus: Calculus may not be one of the most significant parts to build an ML model, but to comply with minimizing complexity equations of algorithms or certain aspects such as gradient descent, derivatives come much handy.

Probability: Regardless to say, the probability is the unspecified hero of machine learning. Being the backbone of data science, all the algorithms primarily work based on the concept of probability only.

2. Knowledge of Programming and Algorithms: Mathematics may be the backbone, but at the end of the day it is a game of computer scientists and their brilliant mindsets to dive deeper into data. So, a sound understanding of algorithms and pseudocode is crucial to come through this hurdle. Often, machine learning algorithms are much more complicated than usual codes and contain multiple segments with different approaches of programming methodology. Such as, a top-down approach may be significant in solving a gradient descent problem while a bottom-up approach can be more relevant in the case of support vector machines.

So, being fluent with codes and pseudocodes is compulsory to learn ML. Sometimes, you may have to go through a series of code segments of thousands of lines to find a single bug. Sound programming knowledge will help you there to understand the thinking of a machine.

3. Building Data Models: At the end game, it’s only a data scientist’s vision to see through piles of unstructured data and find out the underlying phenomena there. So, to fit the proper match model in the data may take turns. Hence, you should be patient and competent enough to manipulate your data models in such a way that you can present outputs in an expected manner.

To conclude, I must say machine learning is a cakewalk, although walking on a cake may slip you down quite a few times. Hence, if you are brave enough to try and try along, indeed, you are on the right path. After all, learning from error and getting close to the correct answer is how machine learning works. So, why not you?

AI/ML

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