Machine Learning (ML) as a Career
Machine Learning (ML) is the subcategory of artificial intelligence (AI). The ML focuses on building systems that learn and improve performance based on the data they consume. Often, Machine learning and AI are discussed together. Sometimes the terms are used interchangeably, but they don’t mean the same thing. In the modern world, ML is all around us. When we do banking, shop online, or use social media, machine learning algorithms come into play to make our experience efficient, smooth, and secure. Machine learning engineers work with algorithms, data, and artificial intelligence.
Prerequisites for Machine Learning (ML) Engineering
- Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.
- Intermediate knowledge of statistics and probability.
- Basic knowledge of linear algebra. In the linear regression model, a line is drawn through all the data points, and that line is used to compute new values.
- Understanding of Mathematics and Statistics.
- Knowledge of how to clean and structure raw data to the desired format to reduce the time taken for decision-making.
Types of Machine Learning
Supervised Machine Learning: This is a frequently used method. In this model a data scientist acts as a guide and teaches the algorithm what conclusions it should make. In supervised learning, the algorithm is trained by a dataset that is already labeled and has a predefined output.
Unsupervised Machine Learning: Unsupervised machine learning uses a more independent approach, in which a computer learns to identify complex processes and patterns without a human providing close, constant guidance. Unsupervised machine learning involves training based on data that does not have labels or a specific, defined output.
ML Engineers Responsibilities
While job responsibilities for machine learning engineers will differ, they often include:
- Implementing machine learning algorithms
- Running AI systems experiments and tests
- Designing and developing machine learning systems
- Performing statistical analyses
Job Prospects for Machine Learning Engineers
Over the past few decades, the computer science field has seen continuous growth. According to the US Bureau of Labor Statistics, information and computer science research jobs will grow 23% through 2032. This prediction is for the USA alone. Opportunities will be limitless in this field.
How to become a machine learning engineer?
To become a ML Engineer you can follow these steps:
1. Earn a bachelor's degree in computer science or a related field like Mathematics and Statistics etc.
2. Gain entry-level work experience. Some entry-level positions that can lead to a machine learning career include:
- Computer engineer
- Data scientist
- Software developer
- Software engineer
3. Build your machine learning expertise.
While working in a related role, you can build specialized experience to prepare you for machine learning engineering. Consider working on machine learning projects to practice essential skills or earning relevant certifications.
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