What are prerequisites to start with the Machine Learning?

    In order to learn machine learning, you should understand the exact concept of machine learning and its ecosystem. We can divide the pre-requisite of machine learning into two parts one is a summary of skills and other is languages and libraries of the framework.

    28th Nov 2018
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    Today, many people are getting interested in machine learning, the field is generating more buzz these days. So, people or aspirants may be interested in machine learning due to many reasons like due to their interest in coding or to get a high paying job or any other, but there are a few pre-requisites to start with machine learning that you should always keep in mind.

    Today, in this post, we will discuss all pre-requisites for this language. To start with machine learning language, a candidate must have some skills that may help them in shaping his career. In this post, we will see an ecosystem of machine learning first and then we will discuss the skills that are required to become a machine learner or to learn this technology.


    Here is a quick view of machine learning prerequisites and we will be discussing each of them in detail later.

                                     

    The Machine Learning Ecosystem

    In order to learn machine learning, you should understand the exact concept of machine learning and its ecosystem. Mainly there are two skills that are like pre-requisite for machine learning one the system itself and second is the relationship of various system components and the interrelationship with each other.

    Even the understand how the system works then there are two ways one is data analysis and other is machine learning. For this, we can take the example of a grocery chain that is going to start giving coupons to its selected customers. The customers will be selected on the basis of their last purchase or purchase history. For data analysis, proper and sufficient data may be required to perform any analysis that will give a trend through which proper strategy can be designed.

    On the other hand, in the case of machine learning, for such scenarios an automated coupon generation system will have to be written. But here the developers may have to understand the complete ecosystem like pricing, inventory, catalog, bill generation, purchase order, CRM software, and POS, etc.

    In other words, we can say that machine learning is less about understanding but it is all about learning and designing algorithm. It can only be understood if you will know and understand the interrelationship between system components only then you can write a system or software that can be integrated with the system. The output of machine learning is working software only.

    A Guide to Machine Learning Pre-Requisites

    We can divide the pre-requisite of machine learning into two parts one is a summary of skills and other is languages and libraries of the framework. Here in this section, we will discuss the skills that are expected for machine learning and why an understanding of algorithms and machine learning libraries is vital.

    • Computer Science Basics and Programming

    Here the computer science fundamental concepts like a stack, queue, tree, graphs, multi-dimensional arrays like data structures and the algorithms of sorting, searching, dynamic programming and optimization are included. Apart from this the complexity and computability concepts like P vs. NP or NP-complete problems, big-O notation, approximation algorithms are included. Along with this computer architecture concept like cache, memory, deadlocks, bandwidth, distributed processing includes.

    One should be able to understand and implement these concepts in computer programming and for this, they have to practice a lot. For this hackathon and coding competitions can help the aspirants

    • Statistics and Probability

    Some popular probability theorems like conditional probability, likelihood, independence, Bayes Rule or Theorem and popular techniques like Markov Decision, Bayes Nets, Hidden Markov Models and other methods are included. They are used to solve the uncertainty problems of the real world.

    Similarly, another related field to statistics is also imperative to be learned as various statistical methods like mean, median, mode, variance concepts are being used. The distribution concepts like uniform, binomial, normal and Poisson are being used in Machine learning along with analysis methods like ANOVA. Hypothesis methods are being used to build, validate models, and observe several models. In machine learning, various algorithms are an extension of statistical models.

    • Data Modeling

    The process of estimation of any underlying structure for a given dataset and to identify the useful pattern like correlations, clusters, Eigenvalues or to predict the instance’s properties like through classification, anomaly, regression, and detection. Here, they key process is to identify the pattern in given data model.

    You may also have to choose error or accuracy method measures like log-loss for classification, regression and the evaluation strategy like splitting of training and testing and sequential and randomized cross-validations along with iterative learning algorithms are often used that may result in errors for that Back-propagation models are usually used. Even understanding of the concept of neural networks is also expected.

    • Machine Learning Algorithms and Libraries

    Machine learning algorithms can be directly applied through libraries or packages or APIs e.g. sci-kit-learn, Spark MLib, H2O or Theano, but to choose one of them understanding of models and libraries is essential for that you may have to choose a decision tree, neural net, support vector machine or nearest neighbor and ensemble of multiple models or methods. The learner must also know the effect of hyper-parameters on learning. You must be aware of the advantages and disadvantages of various approaches.

    • System Design and Software Engineering

    As a machine learning engineer has to deliver a software that is usually being fitted into a large ecosystem of services or products. You must know that how these components will work and communicates with each other usually for this REST APIs, library calls and database queries are being used.

    You must avoid any bottlenecks and scale the algorithms by increasing data volume. Here various software engineering practices like requirement analysis, modularity, version control, system design, documentation, and testing are also required for invaluable collaboration, productivity, maintainability, and quality.

    Final Words:

    For Machine Learning, there are limitless applications and number of fields where the concept can be applied including finance, education, computer science, and others. There is no such field in which machine learning cannot be applied. Even in some fields, the machine learning techniques are desperately needed and the Healthcare industry is the best example here.

    For various healthcare procedures like critical scans, effort reduction in care and others machine learning has done a lot. You may find a number of lectures and research using Machine Learning in Healthcare.

    So, it can be said that the world is changing rapidly and so as the demand for machine learning professionals is also increasing exponentially. As the real-life challenges are quite complex so it is only machine learning that can help in building a proper engineering framework. You can say that machine learning is the future of the IT world certainly.

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    Vikas Arora

    Vikas Arora loves pursuing excellence through writing and have a passion for technology. he has successfully managed and run personal technology magazines and websites. he currently writes for JanBaskTraining.com, a global training company that provides e-learning and professional certification training. His work has been published on various sites related to Oracle DBA, Digital Marketing, Big Data Hadoop, Java, Dot net, SQL, Data Science, Salesforce, AWS, PMP, DevOps, Business Intelligence, Project Management, Cloud Computing, IT, SAP, Project Management and more.

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