Machine learning is a branch of computer science focused on data analysis. Gaining an education in this field can be of value across various industries and help them pursue a career in machine learning. To achieve this, they must first enroll in formal educational programs or make use of online resources for self-learning. Looking into the various ways to build a career in machine learning can greatly benefit aspiring engineers.
Fine-tune math skills
A machine learning expert must have a thorough grasp of various areas of mathematics. Essential skills include algebra, probability, statistics, and multivariable calculus. While an in-depth knowledge of these topics is not mandatory, understanding them can be beneficial when exploring machine learning. Aspiring professionals can rely on online resources, physical books, videos, and articles to gain insight into this subject and hone their math skills. Additionally, hiring a tutor or attending virtual or in-person courses can also be helpful.
Learn a programming language
A programming language serves as a bridge between humans and computers, ensuring effective communication. A commonly used programming language in machine learning is Python, and most machine learning roles require extensive use of Python. Key Python libraries to focus on include NumPy for numerical computations, Pandas for data manipulation, and Matplotlib and Seaborn for data visualization.
Study basic computer science and coding skills
Those who do not have experience in programming should first attempt to learn basic coding skills. They can try to learn the basics on their own or attend training programs to learn to code. Those interested in a career in machine learning should practice writing code in addition to studying the theory. This approach will help them retain and apply the information in real-world situations.
Earn any necessary degrees
Obtaining a degree in computer science or data science is one of the ways to pursue a career in machine learning. One excellent option for this degree is Western Governors University (WGU). WGU offers an ABET-accredited online computer science program focusing on artificial intelligence.
The curriculum includes 37 courses that teach in-demand skills, including artificial intelligence, logic, machine learning, architecture and systems, and data structures. Individuals are required to have prior educational certifications, including a high school diploma or an equivalent qualification. Interested individuals can visit WGU’s website for more information about the program. Additionally, it is worth exploring similar degrees from other universities that might be more tailored to specific interests in machine learning.
Enroll in machine learning programs and certifications
In addition to a degree, individuals can enhance their machine learning skills by enrolling in and completing top programs and certifications to further their careers.
Stanford’s Machine Learning Specialization
The two-month, three-course series covers machine learning at a beginner level. It helps those enlisted build ML models with NumPy and Scikit-learn and train supervised models for prediction and binary classification tasks. The course also includes components on building and training neural networks with TensorFlow for multi-class classification and exploration of decision trees and tree ensemble methods. Individuals can enroll in the course for free on Coursera.
IBM Machine Learning Professional Certificate
The program from IBM helps individuals perfect practical skills and machine learning knowledge needed in daily roles. The coursework on Coursera also helps them develop their working knowledge of KNN, PCA, and non-negative matrix collaborative filtering. This three-month course also covers ML at an intermediate level.
Practice with available datasets
In addition to completing a degree or the programs, individuals should also take time to practice using existing data sets available online. The vast library of previously collected data can help them focus on their learning without the time-consuming process of gathering data. They can select data based on different qualities they wish to practice with. Some examples include Attributes, which are descriptions like data or ages. For example, attributes in a housing price dataset may include the size of the house, the number of bedrooms, and the location. These features can help individuals practice various tasks, such as predicting house prices or identifying consumer behavior. Understanding how to work with datasets is essential for advancing in a machine learning career.
Build a portfolio
Education is only one aspect of building a career in machine learning. In addition, individuals will need to work on their projects and build a portfolio. This will include collecting the necessary data, cleaning it, and using it similarly to established datasets. This will help individuals understand data collection, preprocessing, transformation, model building, and analysis. Over time, individuals will learn how to build a portfolio that showcases their skills to potential employers or clients.
Join an online community
Individuals can also participate in online message boards, social media groups, and chatrooms with others interested in ML. The space will allow them to converse with others worldwide and share learnings and tips.
Work on communication skills
One of the top machine learning career tips for beginners is improving communication skills. Individuals may have to interview in person, over the phone, or online to secure a job and become part of a team. In that case, they will be required to explain complicated concepts to team members, especially those without a computer science background, and listen to their goals and feedback. Individuals may have to observe data and succinctly convey their findings to help collaborators easily understand various trends and situations, enabling quick, informed decisions.
