• Post Graduate Program in Analytics and Artificial Intelligence
    Co-created with Coding Ninjas
    4.8 out of 5 by 4610 learners
    12 X industry demand
  • Machine Learning and Deep Learning Prodegree
    Co-created with IBM
    4.7 out of 5 by 2750 learners
    32 X industry demand
  • Post Graduate Program In Data Analytics
    4.7 out of 5 by 3600 learners
    14 X industry demand
  • Data Science Prodegree
    Co-created with KPMG in India
    4.8 out of 5 by 6071 learners
    16 X industry demand

Machine learning has quickly become the buzzword of recent years. The rise of machine learning to one of the most sought-after fields is unparalleled, and it is easy to see that it is going to play a huge role in the future of the world. Machine learning and AI systems are already being implemented in many of the Fortune 500 giants, and therefore a lot of companies are on the lookout for talented people.
While there are many differences in machine learning vs artificial intelligence, the basic building blocks in order to be proficient in both are the same. You will need to have some skills and domain knowledge, in order to start practicing in the field. Let us take a look at some parts of the process of starting to learn machine learning.
Python or R
These are the commonly used programming languages in machine learning and AI today. Algorithms are implemented in both the areas using any programming language, but these are the ones which are easiest to learn for beginners. Python is disposed towards machine learning and is extremely easy to learn and understand. Many organizations have already used it in places they need to have end-to-end integration, and to develop analytics-based applications. Since Python has a large library support system, it can be easy for users to implement algorithms too.
R, on the other hand, is a workhorse meant to carry out heavy statistical processes. Companies which are highly focused on data analytics prefer R, and it has basically become the lingua franca for data science today.
Statistics
In order to be competent in machine learning, you should have at least a basic understanding of statistics. While initially, you may need to understand what the algorithm actually does – you will only need to know how the tools can be used for your end result. However, after a while, you will need to start implementing your own algorithms or modify existing algorithms well. This means that you will need to have a comprehensive statistics base so that your bases are covered during crunch time. Bayesian probability and some more descriptive and inferential statistics will be necessary at some point or other.
Practice
Online courses are the best answer as to how to learn machine learning. You can supplement your learning experience using many books which are available, and they will help you move from novice to expert in the field. This means that you will start to gain a theoretical understanding of how machine learning is done, but do not get comfortable – machine learning is an art, and all arts need to be practiced. While the theory may be necessary, you should start implementing your knowledge in some form or other so that you learn from experience. There are websites like UCI Machine Learning Repo or Kaggle which have a host of datasets and competitions for you to hone your craft.
If all of this has started to interest you in learning machine learning and AI, you should check out the courses on offer at Imarticus Learning.

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About Imarticus
Imarticus Learning is India’s leading professional education institute that offers training in Financial Services, Data Analytics & Technology. We’ve successfully transformed careers of over 35,000+ individuals globally through our Certification, Prodegree, and Post Graduate programs offered in association with leading and renowned global organisations in the Financial Services, Data Analytics & Technology domain.
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