How to Become a Machine Learning Engineer

By | July 18, 2019

Right now, Artificial Intelligence (AI) and Machine Learning (ML) are two of the hottest fields in technology. By big corporations as well as by start-ups Machine Learning engineers are in high demand. It has gained a lot of momentum in recent years due to the resurgence of Artificial Neural Networks in practical applications. It is at the intersection of statistics and computer science, yet it can wear many different masks. You may also hear it labeled several other names or buzzwords:

Data Science, Artificial Intelligence, Big Data, Computational Statistics, Data Mining, Predictive Analytics, Etc…

To train intelligent systems, these professionals perform sophisticated programming and work with complex data sets and algorithms. Coders and programmers with solid data skills can transition to become machine learning engineers, though they may need experience in a data role beforehand. 

  • Learn techniques and algorithms and how to package and deploy your models to a production environment. 
  • Gain practical experience using Amazon SageMaker to deploy trained models to a web application and evaluate the performance of your models.

Not many IT specialists have direct experience with it, ML is an emerging role. That’s why most machine learning engineer job descriptions today seek out data scientists with a programming background. 

What does a Machine Learning Engineer do?

In practical terms, the role of a machine learning engineer is close to that of a data scientist. Both roles work with vast quantities of information, require exceptional data management skills, and the ability to perform complex modeling on dynamic data sets.

Machine learning engineers typically includes the following:

  1. Advanced degree in computer science, math, statistics or a related discipline
  2. Extensive data modeling and data architecture skills
  3. Programming experience in Python, R or Java
  4. Background in machine learning frameworks such as TensorFlow or Keras
  5. Knowledge of Hadoop or other distributed computing systems
  6. Experience working in an Agile environment
  7. Advanced math skills (linear algebra, Bayesian statistics, group theory)
  8. Strong written and verbal communications.

Why Machine Learning Engineers are in Demand?

Attempting to process all that information manually is like drinking from a fire hose. Applications of ML are as varied as the data itself. A few common applications include: 

  • Image and speech recognition — Machine learning excels at auto-tagging images, text-to-speech conversions and anything else that requires turning unstructured data into useful information.
  • Drives the algorithms at the heart of e-commerce, telling consumers who buy product A that they might like product X. The way ML software makes connections
  • Risk management and fraud prevention — ML algorithms can analyze huge volumes of historical data to make financial predictions, from future investment performance to the risk of loan defaults. Regression testing also makes it easier to spot fraudulent transactions in real-time.

Skills Needed To Become a Machine Learning Engineer

In order to be a successful machine learning engineer, the prerequisites that are a must are:

Basics of Python Programming Language

In recent years, Python has topped the charts over other programming languages like C, C++, and Java and is widely used by the programmers. 

  • The Python 1.0 had the module system of Modula-3 and interacted with Amoeba Operating System with varied functioning tools. 
  • In the year 2000 Python 2.0 introduced had features of the garbage collector and Unicode Support.
  • In the year 2008 Python 3.0 introduced had a constructive design that avoids duplicate modules and constructs. 
  • Now the companies are using Python 3.5 with the added features.
Advantages Disadvantages
Extensive Support Libraries Difficulty in Using Other Languages
Integration Feature (COM or COBRA) Weak Language for Mobile Computing
Improved Programmer’s Productivity (using languages like Java, VB, Perl, C, C++, and C#). Gets Slow in Speed
Productivity of applications Run-time Errors

Data Manipulation at Scale: Systems and Algorithms

Linear algebra is a part of mathematics concerned with matrices, vectors, and linear transforms.

Review 10 concrete examples of linear algebra in machine learning – 

  • Dataset and Data Files
  • Images and Photographs
  • One-Hot Encoding
  • Linear Regression
  • Regularization
  • Principal Component Analysis
  • Singular-Value Decomposition
  • Latent Semantic Analysis
  • Recommender Systems
  • Deep Learning

Calculus in Data Science

The calculus is divided into 2 parts. Need to understand for deep learning

  1. Differential Calculus cuts something into small pieces to find how it changes.
  2. Integral Calculus joins (integrates) the small pieces together to find how much there is.

Statistics and Probability

Few techniques such as Bayes nets, hidden Markov models and all these concepts. And then statistics is really simple, right? Mean, median, variance and all. Even distributions like normal, binomial, what else, yeah, poison and even uniform distribution.

ML Frameworks

In frameworks like Keras, Tensorflow, Pytorch, etc, learn how to build and run ML models. If you’re just beginning, start with Keras which acts as a frontend to frameworks like TensorFlow and abstracts lower-level details. It’s a great starting point.

How to cast your Machine Learning Engineer Resume?

The weakest part on most resumes of data professionals seeking an ML role is a lack of programming experience. If this is you, focus on honing your coding skills. Python is the most popular programming language in ML. The big reason is that it’s relatively easy to learn, but Python also is extremely well supported with powerful machine learning libraries. R is also commonly used, and you may need Java and/or C++ for developing applications.

One way to do that is to join Kaggle, the Google-owned site that bills itself as “the place to do data science projects.” For experiments, this online group share datasets and offers many courses to help you develop and brush up on your skills.

Take courses from Amazon — the machine learning giant and creator of Alexa. Amazon Web Services (AWS) offers ML training and certification with four paths: developer, business decision-maker, data scientist and data platform engineer. Best of all, they’re free.

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