Machine learning, a subset of artificial intelligence (AI), has become an indispensable part of our world. Its applications span various industries, including healthcare, finance, agriculture, e-commerce and entertainment. From recommendation systems that suggest products you might like to advanced image recognition used in autonomous vehicles, machine learning is pivotal in enhancing efficiency, accuracy and decision-making.
As the importance of machine learning continues to grow, so does the demand for professionals skilled in this field. The share of job postings for machine learning engineers increased by 53% between the start of 2020 and the start of 2023.1 According to the World Economic Forum, AI and machine learning specialists top the list of fastest-growing jobs.2 In a landscape where AI threatens to automate many jobs, those who can leverage machine learning techniques to extract meaningful insights from data and develop predictive models are invaluable to companies.
Becoming a proficient machine learning professional requires a diverse skill set encompassing technical, mathematical and practical abilities. In this guide, we will delve into the core skills and requirements that you should acquire to find your place in this dynamic field, as well as the soft skills that will help you stand out.
Core Machine Learning Skills
In this blog post, we’re going in-depth on the core technical skills needed to build a successful career in machine learning. These core skills include:
- Proficiency in programming languages
- Strong understanding of mathematics and statistics
- Knowledge of machine learning algorithms and techniques
- Experience with data manipulation and preprocessing
- Ability to work with large datasets and data visualization
- Problem-solving ability to overcome unexpected problems and challenges
Let’s take a look at each of these in detail.
Programming and Software Development Skills
One of the fundamental technical skills in machine learning is proficiency in programming languages, with Python and R being the most popular choices. These languages offer a plethora of libraries and tools specifically designed for machine learning tasks, making them essential for data manipulation, model development and deployment.
Expertise in Python Programming Language
Python is an essential language for machine learning due to its simplicity, readability and a rich ecosystem of libraries. Proficiency in Python allows professionals to leverage libraries like NumPy, Pandas and Matplotlib for data manipulation and visualization.3
Knowledge of Relevant Libraries and Frameworks
Beyond Python, knowledge of machine learning libraries and frameworks is crucial. TensorFlow, PyTorch, scikit-learn and Keras are some of the widely used libraries for building and deploying machine learning models.4 Familiarity with these tools is highly beneficial.
Experience in Version Control Systems
Version control systems like Git are essential for collaborative machine learning projects. They help track changes in code and facilitate teamwork, ensuring that projects remain organized and errors are minimized.5
Understanding of Software Development Principles
Machine learning is not just about building models but also about developing robust, maintainable software. Understanding software development principles, such as an architecture-first approach, iterative life cycle processes and change management systems, is essential for creating reliable machine learning applications.6
Mathematical and Statistical Skills
Mathematics forms the bedrock of machine learning. A strong foundation in mathematics, including linear algebra, calculus and probability theory, is crucial for understanding the underlying principles of machine learning algorithms and their optimization. You need to learn math in the context of machine learning—to be able to interpret equations and develop an intuition for them.
Solid Foundation in Linear Algebra and Calculus
Linear algebra and calculus are at the core of many machine learning algorithms, particularly in deep learning. Concepts like matrix operations, derivatives and gradients are fundamental to understanding how models are trained and optimized.7
Understanding of Probability and Statistics Concepts
Probability theory and statistics are essential for making sense of data and drawing meaningful conclusions. Concepts like probability distributions, hypothesis testing and Bayesian inference are commonly used in machine learning.7
Familiarity with Multivariate Calculus and Gradient Descent
Multivariate calculus extends the understanding of derivatives to multiple dimensions, which is particularly important for deep learning. Gradient descent, a common optimization technique, relies heavily on multivariate calculus.7
Data Manipulation and Preprocessing Skills
Real-world data is often messy and unstructured. Machine learning practitioners need the skills to clean, preprocess and transform data into a suitable format for model training. This includes handling missing data, dealing with outliers and performing feature engineering.
Ability to Clean and Preprocess Data
Real-world data is often messy, with missing values and outliers. Machine learning professionals should be skilled in data cleaning and preprocessing techniques to ensure that models are trained on high-quality data.
Knowledge of Feature Selection and Engineering
Feature selection and engineering involve choosing the most relevant attributes for modeling and creating new features to improve model performance.8 Feature engineering bridges the raw data and the algorithm, meaning these skills help enhance the predictive power of machine learning models.
Understanding of Data Normalization and Scaling Methods
Data normalization and scaling ensure that features are on the same scale, preventing certain features from dominating the learning process. Normalization techniques like Min-Max scaling or Z-score normalization are commonly employed. Other techniques include decimal scaling, logarithmic transformation and root transformation.9
Machine Learning Algorithms and Techniques
Machine learning professionals must be well-versed in a wide range of algorithms and techniques, such as supervised learning (e.g., linear regression, support vector machines), unsupervised learning (e.g., clustering, dimensionality reduction) and reinforcement learning.10 A deep understanding of when and how to apply these methods is vital.
Familiarity with Popular Supervised Learning Algorithms
Supervised learning is a cornerstone of machine learning, and professionals should be familiar with various algorithms, including linear regression, decision trees, support vector machines, and ensemble methods like random forests.10
Understanding of Unsupervised Learning Techniques
Unsupervised learning, which includes clustering and dimensionality reduction, is essential for tasks like customer segmentation and anomaly detection. Machine learning practitioners should be well-versed in techniques such as k-means clustering and principal component analysis (PCA).10
Knowledge of Deep Learning Models and Neural Networks
Deep learning has revolutionized many areas of machine learning, including computer vision and natural language processing. Proficiency in deep learning frameworks like TensorFlow and PyTorch, as well as neural network architectures, is highly valuable.11
Experience with Model Evaluation and Validation Methods
Evaluating and validating machine learning models is critical to ensure their reliability and generalization to new data. Cross-validation, metrics like accuracy and F1 score, and techniques like hyperparameter tuning are essential tools in a machine learning practitioner's toolkit.12
Big Data and Distributed Computing Skills
In our data-driven world, handling large datasets is common. Machine learning professionals should be comfortable working with big data tools and have the ability to extract meaningful insights from these vast datasets. Additionally, data visualization skills are essential for conveying results and insights effectively.
Understanding of Distributed Computing Frameworks
With the advent of big data, machine learning professionals often encounter distributed computing frameworks like Apache Hadoop and Apache Spark.13 Understanding these frameworks is crucial for processing and analyzing large-scale datasets.
Ability to Work with Big Data Tools
In addition to distributed computing frameworks, machine learning practitioners should be familiar with big data tools like Apache Kafka for data streaming and Apache Cassandra for distributed database management.14
Knowledge of Cloud Platforms
Cloud platforms like AWS, Google Cloud Platform and Microsoft Azure offer scalable infrastructure and services for machine learning projects. Proficiency in using cloud resources for data storage, computation and deployment is advantageous.
Soft Skills for Machine Learning Professionals
Soft skills complement technical expertise and often set exceptional technical professionals apart. As technology advances, the value of soft skills, or a “human touch” in technical positions is likely to grow, making them an essential component of a successful career in the tech industry.
Soft skills that are beneficial to a machine learning career include:
Strong Problem-Solving and Critical Thinking Abilities
Machine learning projects frequently involve complex problems that require creative and critical thinking. You must approach challenges with creativity, flexibility and the ability to think outside the box. Memorizing information from blog posts and books will only get you so far. A well-rounded master’s program can be especially beneficial to growing your skills in this area. For instance, Milwaukee School of Engineering’s online machine learning programs are focused on helping you develop the problem-solving skills needed to move projects forward when unexpected problems arise.
Effective Communication and Collaboration Skills
Machine learning professionals often work in multidisciplinary teams. Effective communication and collaboration skills are essential for conveying technical concepts to non-technical stakeholders and collaborating with data engineers, domain experts and business analysts. You may also work in a customer-facing role, making strong communication skills paramount to the success of your projects. Overall, communication bridges the gap between technical and business perspectives.
Leadership and Decision-Making
As you advance in your machine learning career, you may be called upon to lead teams, make strategic decisions and manage projects. Leadership and decision-making skills become increasingly important as your career evolves and grows.
Continuous Learning Mindset and Adaptability
The field of machine learning is constantly evolving. You must maintain a commitment to continuous learning by staying updated with the latest techniques, tools and best practices to remain competitive.
Master Essential Machine Learning Skills With MSOE
As a current or aspiring machine learning professional, you should prioritize acquiring a diverse skill set to tackle the complex challenges and opportunities of machine learning and AI. Earning a certificate or master’s in machine learning is a great way to grow your skills and expertise.
MSOE’s online machine learning programs were designed by engineers for engineers. Through a comprehensive curriculum, you will develop problem-solving skills, practical and technical skills, and the conceptual knowledge to successfully handle unexpected problems and be resilient as you face challenges throughout your career.
Choose from the online Graduate Certificate in Applied Machine Learning or the online Master of Science in Machine Learning. No matter which program you start with, MSOE’s online programs will give you the essential machine learning skills you need to succeed in this in-demand field. Connect theory to application with a curriculum that focuses on hands-on learning.
- Retrieved on September 25, 2023, from indeed.com/career-advice/news/best-jobs-of-2023
- Retrieved on September 25, 2023, from weforum.org/agenda/2023/05/jobs-lost-created-ai-gpt/
- Retrieved on September 25, 2023, from geeksforgeeks.org/7-skills-needed-to-become-a-machine-learning-engineer/
- Retrieved on September 25, 2023, from hackr.io/blog/best-machine-learning-libraries
- Retrieved on September 25, 2023, from neptune.ai/blog/version-control-for-ml-models
- Retrieved on September 25, 2023, from geeksforgeeks.org/modern-principles-of-software-development/
- Retrieved on September 25, 2023, from geeksforgeeks.org/machine-learning-mathematics/
- Retrieved September 25, 2023, from towardsdatascience.com/what-is-feature-engineering-importance-tools-and-techniques-for-machine-learning-2080b0269f10
- Retrieved on September 25, 2023, from geeksforgeeks.org/data-normalization-in-data-mining/
- Retrieved on September 25, 2023, from.javatpoint.com/machine-learning-algorithms
- Retrieved on September 25, 2023, from developer.nvidia.com/deep-learning-frameworks
- Retrieved on September 25, 2023, from v7labs.com/blog/f1-score-guide
- Retrieved on September 25, 2023, from https://levelup.gitconnected.com/from-hadoop-to-spark-an-in-depth-look-at-distributed-computing-frameworks-23e453c5f954
- Retrieved on September 25, 2023, from softwaretestinghelp.com/big-data-tools/