Top 6 Skills to Become a Machine Learning Engineer

Interested in becoming one of the top Machine Learning Engineers driving the AI scene? Well, get in line. With the increasing popularity and need for AI, the number of people looking to make it big in machine learning has drastically increased.

However, these terms are often thrown around as mere buzzwords, with aspirants and even techies unsure of what they actually mean or the skills required to pursue a career in these areas. In this article, I will help you explore the essential skills to become a machine learning engineer that may help you land your dream job!

Before we get into the skills bit, let’s first understand who exactly a Machine Learning Engineer is and how their role differs from that of a Data Scientist or Data Analyst. While there are overlaps in the job descriptions of these roles, the key difference lies in their focus.

Who is a Machine Learning Engineer?

Data Scientists and Data Analysts primarily concentrate on extracting insights from data and presenting them to decision-makers within an organization. They possess some knowledge of Machine Learning algorithms, but it is not their area of expertise.

On the other hand, Machine Learning Engineers specialize in the field of Machine Learning and are primarily focused on creating software components, and algorithms, and shaping models that can autonomously analyze data and provide valuable insights. As such, their specialization lies in computer fundamentals and software development.

The buzz for Machine Learning Engineers is here to say that they aren’t just vital to big tech businesses but the role also offers substantial career growth and salary packages (the highest going up to 20L per annum).

Make sure you understand machine learning fundamentals like Python, SQL, deep learning, data cleaning, and cloud services before we explore them in the next section. You should consider joining GUVI’s Artificial Intelligence & Machine Learning Course, which covers tools like Pyspark API, Natural Language Processing, and many more and helps you get hands-on experience by building real-time projects.

Instead, if you would like to explore Python through a Self-Paced course, try GUVI’s Python course.

Must-Have Skills for a Budding Machine Learning Engineer

To become a successful Machine Learning Engineer, you need to possess a combination of foundational and advanced skills. While some of these skills may already be familiar to you, others may require further development and domain-specific knowledge.

Let’s explore them below:

1. Applied Mathematics

Mathematics forms a crucial skill in the arsenal of a Machine Learning Engineer. Although it may be a subject you encountered in school, its importance in Machine Learning cannot be understated. Mathematics enables you to apply various formulas to select the most appropriate Machine Learning algorithm for a given dataset, set parameters, and approximate confidence levels.

A strong foundation in mathematics, including topics such as linear algebra, probability, statistics, multivariate calculus, and distributions like Poisson, Normal, and Binomial, is essential. Additionally, having knowledge of physics concepts can also be beneficial in your journey as a Machine Learning Engineer.

2. Computer Science Fundamentals and Programming

A solid understanding of Computer Science fundamentals and programming is another essential requirement for an ML Engineer. Familiarity with data structures such as stacks, queues, trees, and graphs along with algorithms such as searching, sorting, dynamic programming, greedy programming, and space and time complexity is crucial.

If you have a bachelor’s degree in computer science, you likely already possess these foundational skills. Proficiency in programming languages like Python and R is also necessary, as they are widely used in Machine Learning and Data Science.

Additionally, familiarity with tools and languages like Spark, Hadoop, SQL, and Apache Kafka can be very advantageous.

3. Machine Learning Algorithms

A comprehensive understanding of common Machine Learning algorithms is vital for any aspiring Machine Learning Engineer. These algorithms can be categorized into three main types: supervised, unsupervised, and reinforcement learning algorithms.

Some common Machine Learning Algorithms include Naive Bayes Classifier, K-Means Clustering, Support Vector Machine, Apriori Algorithm, Linear Regression, Logistic Regression, Decision Trees, and Random Forests. It is crucial to have a solid knowledge of these algorithms and understand their applications in different scenarios.

4. Data Modeling and Evaluation

Skills in data modeling and evaluation are an absolute must-have! Data modeling involves understanding the underlying structure of the data and identifying patterns that may not be immediately apparent.

Additionally, you need to evaluate the data using suitable algorithms based on the type of analysis required, such as regression, classification, clustering, or dimension reduction. The choice of algorithm depends on the specific characteristics of the data.

For example, a classification algorithm suitable for large datasets and speed might be Naive Bayes, while a regression algorithm focusing on accuracy could be Random Forest. To contribute effectively to data modeling and evaluation, you need to be well-versed in various algorithms and their applications.

5. Neural Networks

Neural Networks play a vital role in the career aspects of a Machine Learning Engineer. These networks are inspired by the structure and functionality of neurons in the human brain. They consist of multiple layers, including an input layer, hidden layers, and an output layer.

Neural Networks enable parallel and sequential computations that allow for data analysis and learning. Understanding the fundamentals of Neural Networks is crucial for a Machine Learning Engineer.

While there are various types of Neural Networks, such as Feedforward, Recurrent, Convolutional, Modular, and Radial basis function networks, it is important to have a solid understanding of the core concepts and be open to continuous learning and exploration of different network architectures.

Would you like to learn more about this fascinating concept? Do give our blog on Neural Network Skills to Succeed in Data Science a read!

6. Natural Language Processing

Natural Language Processing (NLP) is a fundamental aspect of Machine Learning. NLP focuses on teaching computers to understand and interpret human language, enabling better communication between humans and machines.

There are several libraries available for NLP, each providing a foundation for tasks such as syntactic analysis, phrase extraction, and word removal. Familiarity with libraries like the Natural Language Toolkit (NLTK) can be beneficial for creating NLP applications.

While you may not need to be an expert in all aspects of NLP, having a good understanding of its principles and some practical experience can greatly enhance your skills as a Machine Learning Engineer.

Read more on NLP in our in-depth article Unlocking the Power of NLP: A Beginner’s Guide to Natural Language Processing.

Kickstart your Machine Learning journey by enrolling in GUVI’s Artificial Intelligence & Machine Learning Course where you will master technologies like matplotlib, pandas, SQL, NLP, and deep learning,  and build interesting real-life machine learning projects.

Alternatively, if you would like to explore Python through a Self Paced course, try GUVI’s Python certification course.

Takeaways…

Machine Learning is everywhere now from medicine, cybersecurity, automobiles to currency and even bio-diversity! If you have a passion for machine learning and want to pursue a career as a Machine Learning Engineer, acquiring the skills we discussed above is quintessential.

Embrace continuous learning, practice, and build various projects and data pipelines to improve your capabilities and secure your dream job as a Machine Learning Engineer. If you’re just starting out or even an expert looking for a great revision, our Machine Learning 101 Course will be just the right learning partner enriched with exclusive assignments that will help you gain project-building potential!

FAQs

What skills are needed for a machine learning engineer?

For starters, coding is a must, particularly in languages like Python and R. ML engineers need to have a deep understanding of mathematics, statistics, and linear algebra. You must also know machine learning libraries and frameworks, such as TensorFlow or PyTorch as well as all the major algorithms. For a thorough guide on all the must-have skills and how to achieve them, refer to the article above.

What language is best for machine learning?

You probably already know the answer… drumroll please: Python of course! It is the most popular due to its extensive libraries such as TensorFlow and scikit-learn. Another must-know is R owing to its great statistics-focused tasks. But these are just the ones you must start with and definitely know, to gain a deeper understanding about all that you must know, Refer to the guide above.

Does a machine learning engineer require coding?

Yes, as a machine learning engineer, you must have good coding skills in programming languages like Python and knowledge of libraries such as TensorFlow or PyTorch. This is because coding is necessary so that you can develop, implement, and optimize machine learning algorithms, create datasets, and build models. Wondering where you should start? Give our article a read for more clarity.

Who is eligible for machine learning?

Everyone! Really, no matter what background you’re from, you are completely eligible to pursue a booming career in machine learning but so that you don’t waste time here and there, do pick up a good learning resource such as the extensive course by GUVI so that you can master machine learning in a commendable amount of time.

Can a beginner learn machine learning?

Of course, but you must have the determination to take on and master this in-demand skill. It would help if you started with fundamentals like linear regression, and progressed to more complex algorithms. Take the help of online courses and blogs such as the one above by GUVI and start building projects to add to your portfolio and you’ll definitely make it!