Machine Learning workflow path from Scratch

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Machine learning is a part of the process in which you train a computer to perform a certain task. Most of you are lagging in the part of knowing the flow to learn machine learning. Here we mentioned some steps to learn machine learning in a proper flow.


1. Know the approach towards the Programming Language

There may be many languages used for machine learning but approaching best among all matters. Have to be strong in python or R and most importantly should know the platform for learning such stuff. When compared with other languages Python and R be extensively used by ML programmers for development. Better knowledge about at least one language is mandatory 

2. Learn About Probability and Statistics

Machine Learning is mostly dependent on mathematics without math ML doesn't make any sense. The concepts of probability and statistics are binding strongly with ML. Everyone should understand the math behind every algorithm. For developing a new solution or algorithm must know the concepts in depth. And most importantly should know the source or platform where to learn and what to learn. Along with probability theory, you should learn linear algebra, calculus, calculus of variation, Graph theory, and optimization methods. All of the above must know how to implement those with programming languages as mentioned before. Simply knowing the math alone won't take you to master should know-how to combinate everything to developed some product.

3. Know-How to Handle Data

ML is all bout data so in advance, you should know how to work with data. You have to know how to prepare and process data. The data processing includes identifying and handling missing data. Before dive in to develop an algorithm to learn data analysis, it is a mandatory step before applying any algorithm, the data analyst role is playing an important job in Machine learning projects. When people start to learn about data then you can easily understand how to work with the particular algorithms. Try to download many datasets and analyze the category and importance of the fields in the datasets.

4. Know About  Libraries

  There are many libraries, packages are available to make the implementation easier but knowing the knowledge of using those libraries need more practice and study. You should know how to choose your suitable model, know the suitable algorithms, know the approaches towards the data model. 

5.Learn About Algorithms

You should have good math knowledge, programming skills with some stuff of data models, then you can easily dive into the algorithms. The learning algorithm isn't a big deal but the way of learning brings you to grab your promising carrier in ML. Choosing the correct algorithm for a particular problem will give a proper prediction or results, If you fail in it you can't create a better model. While learning algorithms one should also know the Math behind in them, which will let anyone design their own algorithm to built a successful model

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