What are pre-requisites to start learning Machine learning?

Machine Learning, is a buzz word in India and around the world. The concept is definitely a business class flight ticket to the data analysis and regression studies. The proliferation in data science, computer power and tendency to make machine smart is what makes machine learning the future.But like humans, machine learning is simply learning from the past.

So, what are pre-requisites of “learning” machine learning?

The programming language:

(For API)

You must have experience in any of the programming languages so that you are used to on how and in what way the algorithms and syntaxes work.

Experts and I would suggest that you ought to have programming skills in python. There are various platforms that are based on python and the libraries which are used by various sub fields machine learning consists. So when you are starting to gain experience in machine learning or Artificial intelligence as a whole, you must give your initial time to learn python. Frequently used platforms are Pycharm, Pandas and Tensorflow etc. A glance about data structures and algorithms would be a plus point to a protege starting to learn machine learning.

Some other proprietary languages like octave and MATLAB are sometimes used for kick-starting your study in machine learning.

The Mathematics:

(For Algorithms)

To make computer understand things, you must make it work on choosing, predicting and analysing data. The various sub sections of mathematics that are used in machine learning are:

  • Statistics: As referenced, machine learning is all about making your computer/ processor learn. The basics of which starts with sending a lot and lot of data to it. The importance of statistics comes in as statistics is the core of providing data about data. Machine learning is hence also termed as “Applied statistics”. For starters, begin with the knowledge of Bayesian statistics.
  • Probability: It is known for finding the chances for results, inputs and premises that the computer must consider. Conditional probabilities, Bayes theorem and Correlation theories are often used in machine learning algorithms.
  • Linear Algebra: It is the mathematics of 21st Vectors, matrices and linear equations are the components of linear algebra which are used as crux in optimisation techniques in machine learning.
  • Optimization techniques: The algorithms that are used to make your computer actually “Learn” must be stable and efficient enough, taking minimum time to provide accurate and precise output.
  • Calculus: The major part of providing optimal solutions from the perspective of machine is through differential and multivariate calculus. Some algorithms may require to find probabilities using integral calculus like the finding the posterior of Bayesian model.
  • There are other sub sections that will be needed in machine learning algorithms. The best part of it is, internet is a hub of everything and you can learn these concepts and techniques very easily.

The Enthusiasm: For starters, just focus on implementations of machine learning. The eagerness to learn, implement and solve tough but common problems is the most important aspect of concept behind machine learning as a whole. Even if you don’t know anything from the above, you can learn it afresh. Heads up, start exploring.

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Should a machine learning beginner go straight for deep learning?

Artificial Intelligence is a buzzword which is on a voyage to replace most of the human work efforts in the subsequent upcoming years. It is a giant circle of a pool of various applications to make a machine intelligent.

Machine learning is a one of the subsets of AI. Representation learning is a subset of Machine learning. And Deep learning is the crux of Representation learning. Hence, going into deep learning before getting an overview of Machine learning can be non-fruitful. Although, it depends on your end goal for which you started the subject. Why do you even want to learn machine learning and its subclasses?

To answer your question, yes! You can start directly with deep learning as well, but it will need some basics of machine learning. Deep represents a dense network with number of layers. It is teaching machine to actually learn like humans, self-adapt and sustain. Deep learning does not require manual input of anything. It needs a large number of data or “big data.” Big data is a separate theory that you will need to understand about before you kick start with deep learning.

Deep learning is inspired by human nature and has feature detection applications. The base of deep learning is neural network present at different layers. Deep learning is a buzz because it’s uniqueness of using human brain and neural coding as a basis to make computer identify, classify and make use of the stimulus/stimuli given to it.

Professionals recommend the learning path of machine learning to be followed first.  Basics of mathematics like algebra and probability, a bit of statistics, python and probably a basic course implementation of machine learning is suggested.

There is this book named Deep learning Book by Goodfellow, Bengio and Courville, which takes you to deep learning via the basics. This can be a presumed shortcut to directly jump to deep learning without learning much about machine learning. Bayesian Deep learning is another aspect crucial to deep learning protégé.

Although, as the protege is shifting towards the practical approach to study, the competition among Machine learning crash courses and machine learning classes will be at its peak. Practical knowledge par with your limits of imagination and capacity to think. The whole AI and ML and deep learning are things more of a buzz rather than people actually working on it. If you are really interested in the mechanisms, start reading theories and implementing algorithms now.


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