The Complete Guide To Become A Machine Learning Engineer​

How To Become A Machine Learning Engineer

Can you say your computer is intelligent?

So, the answer is NO. Our computer is fast, it can process data and analyze it but it lacks common sense. 

Every complex calculation can be solved with it but it cannot use common sense like us or can think like us. 

So basically if your computer by any means gains that common sense or can make its decision without human interference based on data it collects, that is called machine learning.

  • Machine learning is how the computer analyzes your data and notes the pattern and then makes a decision to bring it into action. Therefore, in Machine Learning artificial intelligence is developed based on experience.
  • Generally, Machine Learning is a subset of AI. You are using it unknowingly. Have you ever wondered how you get a similar recommendation of videos on social sites or YouTube? This is machine learning. It uses statistical methods.
  • Interest in Machine Learning has taken its peak when a Harvard Business Review article named “Data Scientist” said it as “The sexiest job of the 21st
    century”.
  • The exclusive sensation in the job market now is the great demand for machine learning engineers which is day after day growing incredibly with the annual growth of 40%. That is why the value and need for machine learning are rising rapidly.

Prerequisites And Skill Needed To Be A Machine Learning Engineer

Machine learning has a few subsets which offer a potential area of expectation for those who are interested in a career in AI. Those are:-

  • Neural networks are essential for computers to teach them how to think and learn by classifying information, similar to what we humans do. The software can recognize images and predict with a great` level of accuracy.
  • Natural Language Processing (NLP) This provides the ability to understand human language to the machines. It makes it easier for us to interact with machines as it develops the machine to respond in a way human audiences can understand.
  • Deep learning This focuses on machine learning tools and places them to solve problems by making decisions. It can be applied to text, images, and speech to come at a conclusion that mimics human decision making.
To become a Machine Learning Engineer, there is no single step. To get a chance to grab this job, you need to take several steps to understand this subject better.

Learning Good Programming Skills

  • To be called a machine learning engineer,  you need to know how to read and create computer code.  
  • Among all the programming languages, Python is the most popular choice with 57% of machine learning developers using it because Python offers readable code.
  • Python is simple which offers developers the to write good systems and it is also readable by humans which makes it easier. Other languages that are preferred are C++ Java JavaScript is and C.

Machine Learning Algorithm

  • Machine Learning uses programmed algorithms that receive and analyze data taken as input and predict the output within an acceptable range. 
  • Over time they learn and optimize their operations to improve performance developing intelligence as new data is fed to these algorithms. 

Learn The Machine Learning Framework

  • Learning frameworks act as a weapon for developers to analyze and design machine learning models. It helps us to create a strong AI structure without going to any technicalities of the algorithms.
  • Tensor-flow, Google Cloud ML engine Apache mahout, Shogun, Sci-Kit learn, Pytorch or TORCH, H2O these are some famous machine learning frameworks.
  • Among these TensorFlow is more preferable because it is an open-source AI library that uses data flow graphs to build models. 
  • It provides the best functionalities when compared to other popular learning frameworks it allows to create advanced and large-scale neural networks with many layers.

Need Maths Skills

  • Mathematical principles like probability, calculus, linear algebra, statistics, and optimization are essential for machine learning.
  • Topics like
  1. Eigendecomposition of a matrix
  2. LU Decomposition
  3. Principal Component Analysis (PCA)
  4. Singular Value Decomposition (SVD)
  5. QR Decomposition/Factorization
  6. Symmetric Matrices
  7. Orthogonalization & Orthonormalization
  8. Matrix Operations
  9. Projections
  10. Eigenvalues & Eigenvectors
  11. Norms
  12. And Vector spaces are needed to be covered in linear algebra for understanding optimization method needed in machine learning. 

Machine learning has been recently called “doing statistics on a mac” so statistics and machine learning are not so different. 

The basics probability theorem and statistics that will help you to become a machine learning engineer are

  1. Random Variables
  2. Combinatorics
  3. Probability Rules & koAxioms
  4. Bayes’ Theorem, and Expectation
  5. Conditional and Joint Distributions
  6. Standard Distributions(Bernoulli, Binomial, Multinomial, Uniform, and Gaussian) 
  7. Variance
  8. Moment Generating Functions
  9. Maximum Likelihood Estimation (MLE)
  10. Prior and Posterior
  11. Maximum a Posteriori Estimation (MAP) and Sampling Methods. 
  • Some Of The important topics in calculus include 
  1. Differential and Integral Calculus
  2. Vector-Values Functions
  3. Directional Gradient
  4. Jacobian
  5. Laplacian Lagrangian Distribution
  6. Partial derivatives, and Hessian. 
  • Real and Complex Analysis 
  1. Sets and Sequences
  2. Metric Spaces
  3. Single-Valued and topology
  4. Continuous Functions 
  5. limits
  6. Cauchy Kernel
  7. Fourier Transforms
  • Information Theory (Entropy, Information Gain), Manifolds and Function spaces, these topics also can’t be ignored if you want to be machine learning engineer.

Data Modeling And Evaluation

Modelling” means to predict the underlying structure of the given data and find useful patterns by training machine learning algorithms.

The important part of this prediction is that it will continuously evaluate how good the model is.

Software Engineer And System Designing

  • At last, the output of a machine learning engineer is software. This often is a small bit that fits into a larger ecosystem of products and services. 
  • You have to understand that in what way these components work and communicate with each other and make the correct interfaces on which others will depend. 
  • Careful system design is important to avoid hold up so your algorithm scale would perform with ease with an increasing number of data.
  • Software engineering best practices including requirements system design, analysis, documentation, testing are necessary for quality, collaboration, productivity, growth, and maintainability.

Roles and Responsibilities Of Machine Learning Engineer

To analyze the ML algorithm that could be used to solve the given problem and rank them according to the success probability.

  • Perform statistical analysis:- In machine learning, statistical analysis is needed to discover answers to the question that arises from the given data we have. It helps to obtain a common understanding of the data to make predictions.
  • Train and retrain system:-  Training a Machine learning model can be done by mapping between a set of input features and an output target. 
  • Fine-tuning test results:-  It is an important step to enhance the accuracy of the fore-casted result.
  • Work on frameworks:- This helps the developers to build machine learning models more easily and quickly without getting into any trouble with the underlying algorithm.
  • Undertaking machine learning experiments and test:-  In machine learning experiment number of learning runs carried out under different situations and testing is done of model performance to check its accuracy.
  • Designing machine learning programs.
  • Growing a deep learning system to meet the business needs for various use cases.
  • Finally implementing the correct and suitable algorithm of ML.

There Are Many MNCs And Startups Which Are Interested In Hiring Machine Learning Engineer.

  • Few companies are:- DBS, Absolut data, Fractal Analytics, Datadog, Innovacer, Incedo, HappiestMinds, Bridgei2i, Qubole, Mastercard, Paypal, Visa Inc, Bloomreach, Goldman Sachs, EY, Datamatics, Latent view Analytics, Deloitte Analytics, Expedia, and also the well known and famous companies like Apple, Byjus, Jio, Facebook and many more. So, the job of machine learning engineers is the most promising position.
  • As a new machine learning engineer, the starting salary could be 13 lakhs and more. According to the survey, this is one of the trendiest and coolest jobs to have. 
  • In USA Machine learning engineers get an annual pay of about 140 thousand dollars and in the UK it’s about 50,000 pounds and about 13 Lakhs in India. 
  • So this is a well-paid job with lots of opportunities. By 2022, there will be 2.3 million jobs in the field of artificial intelligence and machine learning according to Gartner
  • Machine learning engineer’s salaries and job opportunities are very high as compared to other job profiles.

Advantages Of Completing ML Certification Courses

  • Certification is always regarded as the cherry on the cake. It provides better career opportunities.
  • You get an additional benefit which improves your resume and helps recruiters to finds a potential candidates for interviews.
  • It acts as an assurity to your skills and also confirms that you are professional who will be able to perform his role very well.
  • Candidates with certification are always in high demand. Organizations always look for professionals who are skilled in machine learning to improve their business by using data analytics.
  • It will help you to earn above average salary in data analytics industries. Many companies offer best-in-the-industry salary to those who have extra skills like in machine learning.

Future Of Machine Learning 

  • The things which are currently done manually can be done by machines tomorrow. so, machine learning can be a competitive advantage for top MNC or even startups. 
  • The revolution of machine learning going to stay with us and so does its future.
  • The growth of machine learning is happening exponentially, especially in the field of computer vision. We have made the progress from 26% error in 2011 to 3% which is impactful.
  • Suppose you have 1000 pictures of dogs and you have to characterized them into their respective breeds, it can be done but you have to be a dog expert. But through the computer, you can be done easily and quickly. 
  • This same phenomenon can be applied in the field of medicine. Diabetic Retinopathy  in which the eyes get affected. 
  • To diagnose it an eye exam is required, now in rural areas where there is the paucity of doctors a machine learning device that uses computer vision will be of great benefit.

Natural Language Processing (NLP) and Transformers

  • Language models are algorithms which help the machine to perform all kinds of operation like translating text by understanding the text.
  • Nowadays computers can understand a paragraph very well and at a much deeper level which helps the search system to function more accurately. Thus, now the user will get their result in a more specific way and more accurate.
  • Machine learning architectures those who utilize transformers are increasing in functionality and also in popularity. 
  • The transformers based models are showing amazing growth that measures the quality of translation.

Automotive Industries

  • The best growth of machine learning is being seen in the field of automobiles. Big companies like Google, Tesla, Mercedes Benz, Nissan, etc that had invested in machine learning for innovations. 
  • Machine learning is making progress by changing the definition of “safe” driving.
  • The self-driving car is one of the beat innovation which had been possible with the help of machine learning and there is more progress to be made in the future.

Robotics

  • This topic has the interest of all whether he is a researcher or a common man. Researchers of all over the world are working on enhancing the performance robots that can mimic the human brain. 
  • They are using ML, AI, neural networks, computer vision, and many more technologies to make this possible. In the future, it might be possible to interact with robots that can perform similar work like humans.

Data Science Vs Machine learning.

  • Machine learning fits within data science because Data science is a broad term for multiple disciplines. 
  • Various techniques like supervised clustering and regression are used in machine learning and on the other side the “data” in data science may or may not evolve from a machine or a mechanical process. 
  • The major difference between them is that data science is a broader topic which not only focuses on algorithms and statistics but also takes care of whole data processing methodology.

Difference Between Data Science and Machine Learning

How To Become A Machine Learning Engineer

Conclusion (How To Become A Machine Learning Engineer)

Here we discussed everything about the Machine Learning and How to Become a Machine Learning Engineer. Hope you liked this article.

We have covered all the aspects of any topic that would occur but if you still got any sub topic that has not been covered or you want knowledge about any other topic you can write it below . 

We will be back with another great article that would prove an add to your technical knowledge.

Thanks for reading….

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