Machine learning (ML) is evolving industries from healthcare to finance. With the explosion of its applications, the demand for professionals who can develop, deploy, and maintain these intelligent systems is also mounting. But if you’re considering jumping into this exciting field, you might wonder what skills you need to land that dream ML job.
In this blog post, we will discuss the 12 core skills required for a thriving machine learning career. Machine learning course focus a lot on these skills because they are very important for a machine learning engineer.
12 Skills That You Need to Have for Machine Learning Jobs
Before you start with a machine learning job, you need to have certain skills. These skills are emphasised a lot by Machine Learning courses as well. So, here are some technical and non-technical skills that you must have before you apply for a machine learning job:
1. Fundamental Programming
An ML task has code at its core. Understanding how to program in languages such as Python (the omnipotent ML god) and R is important. This program assumes you are well-versed in data manipulation, analysis, and model building using libraries like NumPy, pandas, scikit-learn, and TensorFlow. Depending on the role and necessity of optimisation, knowledge of Java/C++ or Scala might be advantageous.
Python has an easy learning curve, more libraries (libraries are pre-written code that you can use to save tons of time!), and is incredibly flexible for the structure of data analysis as well as model building. A lot of machine learning interview questions are asked in programming.
2. Mathematics
At the end of the day, machine learning is maths. Having good knowledge in linear algebra, calculus, probability and statistics gives you the muscle to comprehend complex algorithms, work efficiently on your data, and explain all those models.
3. Algorithms
There are a variety of ML algorithms, each with its pros and cons. Common ones you should know include Linear Regression, Decision Trees, and Support Vector Machines. As you get better, you will eventually look at deep learning and natural language processing. Machine learning courses focus a lot on algorithms. Moreover, this is also an area where machine learning interview questions are asked.
4. Data Wrangling
To train the model, you need to clean your data. You have to carry out different activities such as handling missing values in columns and outliers. Data wrangling is not the best part of this job, but it goes a long way toward building a robust model.
5. Data Visualization
Data visualization is a basic necessity of ML communication. A skill that will help you turn insights from incredibly complex data into easy visuals. Understand how to generate your private charts/plots with libraries such as Matplotlib, Seaborn, and Plotly, which can be embedded in dashboards for simple-to-interpret results – for both technical and non-technical audiences. If you’re well-versed in data visualisation, then you’ll have no difficulty in answering all the machine learning interview questions from this area.
6. Evaluation of the Model
Developing a model is just half the task. You have to track its performance and then optimise it for better results. It shows you how well the model is predicting each class and what it misses – seen with accuracy, precision, and recall, just to name a few.
7. Big Data
If your data sets are too large and complex, sometimes you will need to use tools like Google Cloud Platform (GCP) or Amazon Web Services (AWS). Hadoop – Distributed applications for big problems. Despite the tools, one of the benefits of big data is that people can deal with it effectively.
8. Version Control
Be careful with your version control in ML projects. Tools such as Git help you keep changes in one place, time-travel, and share your work with other team members effectively.
9. Communication
You will be collaborating with other data scientists, engineers, or product managers. The most important one is to be able to convey a rather complex technical idea in simple and understandable terms. If you have good communication skills, then you’ll be able to answer every machine-learning interview question with ease.
10. Problem-Solving
Machine learning is an iterative process. Yes, there will be unexpected twists and turns. Gain more familiarity and confidence in solving problems through experimentation, using the experiments you perform as input to build your model.
11. Curiosity and Learning
Machine learning is an ever-evolving field. Loving to learn and staying current on new research and advancements in the field is critical to remaining relevant and competitive. You should be able to adapt to new things, learn new tools and techniques every time, and stay updated with the latest happenings.
12. Collaborative Drive
ML projects are rarely conducted alone. They involve different audiences, like data engineers and software engineers who are not deeply into the domain itself. It also includes developers of Hadoop systems or similar technologies. Create an energised, empowered, and capable team culture aligned with high values and a spirit of performance.
Conclusion
To be successful as a machine learning engineer, you need to have the perfect mix of technical and non-technical skills. So, by concentrating on the above-mentioned areas, you will be a step closer to becoming an indispensable part of this dynamic field. However, never forget that it’s the journey that matters and not the destination. Keep a learning mindset and have fun as you level up from a beginner to an expert in this field.
