Machine Learning

Machine Learning, one of the hottest fields of this generation and also the one for highly skillful people. Being a Machine Learning Engineer is not meant for everyone. It can’t be achieved overnight. It takes a lot of effort and dedication to be one.

But it isn’t impossible as well. Just the right plan and the resources to execute it properly and you will eventually be there one day.

So, here’s everything you gotta indulge yourself in if you aspire to have a career in Machine Learning.


CS Fundamentals
Computer science fundamentals are crucial for Machine Learning engineers which includes data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.).

  • Programming Language
    The choice of programming language for Machine Learning is pretty important as it decides your complete ML journey. Generally, Python has been the most preferred language for carrying out ML processes because of the fact that it has got some really extension libraries and frameworks to support them.

  • Probability and Statistics
    A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many Machine Learning algorithms; these are all meant to deal with uncertainty in the real world.
    *** Data Modeling and Evaluation**
    Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, vectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.). A key part of this estimation process is continually evaluating how good a given model is.
    *** Software Engineering and System Design**
    At the end of the day, a Machine Learning engineer’s typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services.

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